Rich formatting of annotated clinical documentation, and related methods and apparatus

ABSTRACT

Systems and methods for producing and presenting annotations of clinical documents in a rich format are described, for instance for use with medical billing procedures. An initial XHTML document documenting a medical patient encounter and having rich formatting is used to generate a plain text document. A clinical language understanding system generates annotations, such as medical codes, which are used to annotate the XHTML document. The annotated XHTML document is then presented to a user, thus displaying for the user the annotations while retaining the rich formatting of the initial XHTML document.

BACKGROUND

Medical documentation is an important process in the healthcareindustry. Most healthcare institutions maintain a longitudinal medicalrecord (e.g., spanning multiple observations or treatments over time)for each of their patients, documenting, for example, the patient'shistory, encounters with clinical staff within the institution,treatment received, and/or plans for future treatment. Suchdocumentation facilitates maintaining continuity of care for the patientacross multiple encounters with various clinicians over time. Inaddition, when an institution's medical records for large numbers ofpatients are considered in the aggregate, the information containedtherein can be useful for educating clinicians as to treatment efficacyand best practices, for internal auditing within the institution, forquality assurance, etc.

Historically, each patient's medical record was maintained as a physicalpaper folder, often referred to as a “medical chart”, or “chart”. Eachpatient's chart would include a stack of paper reports, such as intakeforms, history and immunization records, laboratory results andclinicians' notes. Following an encounter with the patient, such as anoffice visit, a hospital round or a surgical procedure, the clinicianconducting the encounter would provide a narrative note about theencounter to be included in the patient's chart. Such a note couldinclude, for example, a description of the reason(s) for the patientencounter, an account of any vital signs, test results and/or otherclinical data collected during the encounter, one or more diagnosesdetermined by the clinician from the encounter, and a description of aplan for further treatment. Often, the clinician would verbally dictatethe note into an audio recording device or a telephone giving access tosuch a recording device, to spare the clinician the time it would taketo prepare the note in written form. Later, a medical transcriptionistwould listen to the audio recording and transcribe it into a textdocument, which would be inserted on a piece of paper into the patient'schart for later reference.

Currently, many healthcare institutions are transitioning or havetransitioned from paper documentation to electronic medical recordsystems, in which patients' longitudinal medical information is storedin a data repository in electronic form. Besides the significantphysical space savings afforded by the replacement of paperrecord-keeping with electronic storage methods, the use of electronicmedical records also provides beneficial time savings and otheropportunities to clinicians and other healthcare personnel. For example,when updating a patient's electronic medical record to reflect a currentpatient encounter, a clinician need only document the new informationobtained from the encounter, and need not spend time entering unchangedinformation such as the patient's age, gender, medical history, etc.Electronic medical records can also be shared, accessed and updated bymultiple different personnel from local and remote locations throughsuitable user interfaces and network connections, eliminating the needto retrieve and deliver paper files from a crowded file room.

Another modern trend in healthcare management is the importance ofmedical coding for documentation and billing purposes. In the medicalcoding process, documented information regarding a patient encounter,such as the patient's diagnoses and clinical procedures performed, isclassified according to one or more standardized sets of codes forreporting to various entities such as payment providers (e.g., healthinsurance companies that reimburse clinicians for their services). Inthe United States, some such standardized code systems have been adoptedby the federal government, which then maintains the code sets andrecommends or mandates their use for billing under programs such asMedicare.

For example, the International Classification of Diseases (ICD)numerical coding standard, developed from a European standard by theWorld Health Organization (WHO), was adopted in the U.S. in versionICD-9-CM (Clinically Modified). It is mandated by the Health InsurancePortability and Accountability Act of 1996 (HIPAA) for use in codingpatient diagnoses. The Centers for Disease Control (CDC), the NationalCenter for Health Statistics (NCHS), and the Centers for Medicare andMedicaid Services (CMS) are the U.S. government agencies responsible foroverseeing all changes and modifications to ICD-9-CM, and a new versionICD-10-CM is scheduled for adoption in 2015.

Another example of a standardized code system adopted by the U.S.government is the Current Procedural Terminology (CPT) code set, whichclassifies clinical procedures in five-character alphanumeric codes. TheCPT code set is owned by the American Medical Association (AMA), and itsuse is mandated by CMS as part of the Healthcare Common Procedure CodingSystem (HCPCS). CPT forms HCPCS Level I, and HCPCS Level II adds codesfor medical supplies, durable medical goods, non-physician healthcareservices, and other healthcare services not represented in CPT. CMSmaintains and distributes the HCPCS Level II codes with quarterlyupdates.

Conventionally, the coding of a patient encounter has been a manualprocess performed by a human professional, referred to as a “medicalcoder” or simply “coder,” with expert training in medical terminologyand documentation as well as the standardized code sets being used andthe relevant regulations. The coder would read the availabledocumentation from the patient encounter, such as the clinicians'narrative reports, laboratory and radiology test results, etc., anddetermine the appropriate codes to assign to the encounter. The codermight make use of a medical coding system, such as a software programrunning on suitable hardware, that would display the documents from thepatient encounter for the coder to read, and allow the coder to manuallyinput the appropriate codes into a set of fields for entry in therecord. Once finalized, the set of codes entered for the patientencounter could then be sent to a payment provider, which wouldtypically determine the level of reimbursement for the encounteraccording to the particular codes that were entered.

SUMMARY

One type of embodiment is directed to a method comprising: converting amedical document having rich formatting into a first XHTML document;generating a plain text document from the first XHTML document;generating one or more annotations of the plain text document byapplying a natural language understanding (NLU) engine implemented on aprocessor to the plain text document; and creating an annotated XHTMLdocument by applying the one or more annotations of the plain textdocument to a tokenized XHTML document representing the medicaldocument.

Another type of embodiment is directed to a computer-readable storagemedium having instructions that, when executed by a processor, causeperformance of a method comprising: converting a medical document havingrich formatting into a first XHTML document; generating a plain textdocument from the first XHTML document; generating one or moreannotations of the plain text document by applying a natural languageunderstanding (NLU) engine implemented on a processor to the plain textdocument; and creating an annotated XHTML document by applying the oneor more annotations of the plain text document to a tokenized XHTMLdocument representing the medical document.

Another type of embodiment is directed to a system, comprising aprocessor, and a memory coupled to the processor and storingcomputer-readable instructions which, when executed by the processor,cause performance of a method comprising: converting a medical documenthaving rich formatting into a first XHTML document; generating a plaintext document from the first XHTML document; generating one or moreannotations of the plain text document by applying a natural languageunderstanding (NLU) engine implemented on a processor to the plain textdocument; and creating an annotated XHTML document by applying the oneor more annotations of the plain text document to a tokenized XHTMLdocument representing the medical document.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 is a block diagram of an exemplary operating environment for aclinical language understanding (CLU) system that may be employed inconnection with some embodiments;

FIG. 2 is a screenshot illustrating an exemplary graphical userinterface for review of extracted medical facts in accordance with someembodiments;

FIGS. 3A and 3B are screenshots illustrating an exemplary display ofmedical facts in a user interface in accordance with some embodiments;

FIG. 4 is a screenshot illustrating an exemplary display of linkagebetween text and a medical fact in accordance with some embodiments;

FIG. 5 is a screenshot illustrating an exemplary interface for enteringa medical fact in accordance with some embodiments;

FIG. 6 is a block diagram of an exemplary computer system on whichaspects of some embodiments may be implemented;

FIGS. 7A-7F are screenshots illustrating an exemplary user interface fora computer-assisted coding (CAC) system in accordance with someembodiments;

FIG. 8 is a screenshot illustrating an exemplary code finalizationscreen in accordance with some embodiments;

FIG. 9 is a block diagram of an exemplary computer system on whichaspects of some embodiments may be implemented;

FIG. 10 illustrates a document flow for generating an annotateddocument, according to a non-limiting embodiment of the presentapplication;

FIG. 11 illustrates a method which may be used to generate an annotateddocument in connection with the document flow of FIG. 10, according to anon-limiting embodiment of the present application;

FIGS. 12A-12C illustrate non-limiting examples of documents which may beused in connection with aspects of the present application relating toannotating richly formatted content;

FIG. 13 illustrates a document flow for generating an annotated documentin which tabular content is included, according to a non-limitingembodiment of the present application; and

FIG. 14 illustrates a scheme for developing and providing training datafor a clinical language understanding (CLU) system, according to anon-limiting embodiment of the present application.

DETAILED DESCRIPTION

Aspects of the present application are directed to providing a user withannotated medical records in rich formats. In some embodiments, medicaldocuments relating to a patient encounter are processed with a clinicallanguage understanding (CLU) system, for instance using a naturallanguage understanding (NLU) engine of the CLU system, which generatesannotations and evidence. Examples of possible functionality of a CLUsystem are described below. As used herein, the term “annotation” refersto an item derived from and linked to a portion of text, such as a fact(e.g., a medical fact, one particular example of which may be a medicalcode such as a medical billing code), a semantic label, or other suchitem having a link to one or more corresponding portions of text fromwhich it was or could be derived. The annotations generated by a CLUsystem can include, among other things, medically related codes forinformation appearing in the medical document, such as medical diagnosiscodes and/or medical procedure codes, which may function as billingcodes. An annotated document may then be presented to the user includingthe annotations (e.g., the billing codes such as diagnosis and/orprocedure codes generated by the system) together with evidencesupporting the annotations in rich format, allowing the user to readilydiscern the basis and context for the annotations. Such a configurationmay provide greater information to a user than an annotated plain textdocument, which often omits valuable formatting information which couldaid a user in evaluating the provided annotations.

According to some embodiments, presenting an annotated document in richformatting involves conversion of a rich text document to a plain textdocument, generation of annotations of the plain text document, andmapping of the annotations back to a rich text document. For instance, arichly formatted medical record to be annotated (e.g., with diagnosescodes, procedure codes, or other billing codes) may be converted to anXHTML document which is structured to represent the rich formatting ofthe medical record. A plain text document may then be generated from theXHTML document (e.g., by extracting content from the XHTML document) andannotated, for example with codes of the types described above. Atokenized XHTML document may then be generated, and annotations andevidence may be applied to the tokenized XHTML document by anapplication viewer which presents the annotations to a user. In thismanner, the annotations and corresponding evidence may be presented tothe user with the benefit of rich formatting. In some embodiments, theannotations are applied to the XHTML document only in a transientdocument presented to a user (e.g., via a browser), and there is noannotated richly formatted document maintained by the system.

According to an aspect of the present application, a user may edit(e.g., add to, remove from, or revise) annotations (e.g., medical codesand evidence) associated with a richly formatted annotated document, andthe information generated by such editing may be used to train a CLUsystem (e.g., the NLU engine of a CLU system) which generated theannotations. In some embodiments, a richly formatted annotated documentmay be generated as described above and presented to the user. The usermay then select one or more portions of the document and editannotations (e.g., by replacing a code associated with the portion,adding a code, removing a code, and/or linking the selected portion toone or more codes). Information about the portion(s) selected by theuser and the corresponding user-added annotations may then be sent tothe CLU system for training. In this manner, CLU system accuracy may beimproved.

The aspects and embodiments described above, as well as additionalaspects and embodiments, are described further below. While a number ofinventive features for clinical documentation processes are describedabove, it should be appreciated that embodiments of the presentinvention may include any one of these features, any combination of twoor more features, or all of the features, as aspects of the inventionare not limited to any particular number or combination of theabove-described features. The aspects of the present invention describedherein can be implemented in any of numerous ways, and are not limitedto any particular implementation techniques. Described below areexamples of specific implementation techniques; however, it should beappreciate that these examples are provided merely for purposes ofillustration, and that other implementations are possible.

Clinical Language Understanding (CLU) System

An Electronic Health Record (EHR) is an electronic medical record thatgenerally is maintained by a specific healthcare institution andcontains data documenting the care that a specific patient has receivedfrom that institution over time. Typically, an EHR is maintained as astructured data representation, such as a database with structuredfields. Each piece of information stored in such an EHR is typicallyrepresented as a discrete (e.g., separate) data item occupying a fieldof the EHR database. For example, a 55-year old male patient named JohnDoe may have an EHR database record with “John Doe” stored in thepatient_name field, “55” stored in the patient_age field, and “Male”stored in the patient_gender field. Data items or fields in such an EHRare structured in the sense that only a certain limited set of validinputs is allowed for each field. For example, the patient_name fieldmay require an alphabetic string as input, and may have a maximum lengthlimit; the patient_age field may require a string of three numerals, andthe leading numeral may have to be “0” or “1”; the patient_gender fieldmay only allow one of two inputs, “Male” and “Female”; apatient_birth_date field may require input in a “MM/DD/YYYY” format;etc.

Typical EHRs are also structured in terms of the vocabulary they use, asmedical terms are normalized to a standard set of terms utilized by theinstitution maintaining the EHR. The standard set of terms may bespecific to the institution, or may be a more widely used standard. Forexample, a clinician dictating or writing a free-form note may use anyof a number of different terms for the condition of a patient currentlysuffering from an interruption of blood supply to the heart, including“heart attack”, “acute myocardial infarction”, “acute MI” and “AMI”. Tofacilitate interoperability of EHR data between various departments andusers in the institution, and/or to allow identical conditions to beidentified as such across patient records for data analysis, a typicalEHR may use only one standardized term to represent each individualmedical concept. For example, “acute myocardial infarction” may be thestandard term stored in the EHR for every case of a heart attackoccurring at the time of a clinical encounter. Some EHRs may representmedical terms in a data format corresponding to a coding standard, suchas the International Classification of Disease (ICD) standard. Forexample, “acute myocardial infarction” may be represented in an EHR as“ICD-9 410”, where 410 is the code number for “acute myocardialinfarction” according to the ninth edition of the ICD standard.

To allow clinicians and other healthcare personnel to enter medicaldocumentation data directly into an EHR in its discrete structured dataformat, many EHRs are accessed through user interfaces that makeextensive use of point-and-click input methods. While some data items,such as the patient's name, may require input in (structured) textual ornumeric form, many data items can be input simply through the use of amouse or other pointing input device (e.g., a touch screen) to makeselections from pre-set options in drop-down menus and/or sets ofcheckboxes and/or radio buttons or the like.

While some clinicians may appreciate the ability to directly enterstructured data into an EHR through a point-and-click interface, manyclinicians may prefer being unconstrained in what they can say and inwhat terms they can use in a free-form note, and many may be reluctantto take the time to learn where all the boxes and buttons are and whatthey all mean in an EHR user interface. In addition, many clinicians mayprefer to take advantage of the time savings that can be gained byproviding notes through verbal dictation, as speech can often be afaster form of data communication than typing or clicking through forms.

Accordingly, some embodiments described herein relate to techniques forenhancing the creation and use of structured electronic medical records,using techniques that enable a clinician to provide input andobservations via a free-form narrative clinician's note. Someembodiments involve the automatic extraction of discrete medical facts(e.g., clinical facts), such as could be stored as discrete structureddata items in an electronic medical record, from a clinician's free-formnarration of a patient encounter. In this manner, free-form input may beprovided, but the advantages of storage, maintenance and accessing ofmedical documentation data in electronic forms may be maintained. Forexample, the storage of a patient's medical documentation data as acollection of discrete structured data items may provide the benefits ofbeing able to query for individual data items of interest, and beingable to assemble arbitrary subsets of the patient's data items into newreports, orders, invoices, etc., in an automated and efficient manner.

Automatic extraction of medical facts (e.g., clinical facts) from afree-form narration may be performed in any suitable way using anysuitable technique(s), as aspects of the present invention are notlimited in this respect. In some embodiments, pre-processing may beperformed on a free-form narration prior to performing automatic factextraction, to determine the sequence of words represented by thefree-form narration. Such pre-processing may also be performed in anysuitable way using any suitable technique(s), as aspects of the presentinvention are not limited in this respect. For example, in someembodiments, the clinician may provide the free-form narration directlyin textual form (e.g., using a keyboard or other text entry device), andthe textual free-form narration may be automatically parsed to determineits sequence of words. In other embodiments, the clinician may providethe free-form narration in audio form as a spoken dictation, and anaudio recording of the clinician's spoken dictation may be receivedand/or stored. The audio input may be processed in any suitable wayprior to or in the process of performing fact extraction, as aspects ofthe invention are not limited in this respect. In some embodiments, theaudio input may be processed to form a textual representation, and factextraction may be performed on the textual representation. Suchprocessing to produce a textual representation may be performed in anysuitable way. For example, in some embodiments, the audio recording maybe transcribed by a human transcriptionist, while in other embodiments,automatic speech recognition (ASR) may be performed on the audiorecording to obtain a textual representation of the free-form narrationprovided via the clinician's dictation. Any suitable automatic speechrecognition technique may be used, as aspects of the present inventionare not limited in this respect. In other embodiments, speech-to-textconversion of the clinician's audio dictation may not be required, as atechnique that does not involve processing the audio to produce atextual representation may be used to determine what was spoken. In oneexample, the sequence of words that was spoken may be determineddirectly from the audio recording, e.g., by comparing the audiorecording to stored waveform templates to determine the sequence ofwords. In other examples, the clinician's speech may not be recognizedas words, but may be recognized in another form such as a sequence orcollection of abstract concepts. It should be appreciated that the wordsand/or concepts represented in the clinician's free-form narration maybe represented and/or stored as data in any suitable form, includingforms other than a textual representation, as aspects of the presentinvention are not limited in this respect.

In some embodiments, one or more medical facts (e.g., clinical facts)may be automatically extracted from the free-form narration (in audio ortextual form) or from a pre-processed data representation of thefree-form narration using a fact extraction component applying naturallanguage understanding techniques, such as a natural languageunderstanding (NLU) engine. In some embodiments, the medical facts to beextracted may be defined by a set of fact categories (also referred toherein as “fact types” or “entity types”) commonly used by clinicians indocumenting patient encounters. In some embodiments, a suitable set offact categories may be defined by any of various known healthcarestandards. For example, in some embodiments, the medical facts to beextracted may include facts that are required to be documented byMeaningful Use standards promulgated by the U.S. government, e.g., under42 C.F.R. § 495, which sets forth “Objectives” specifying items ofmedical information to be recorded for medical patients. Such factscurrently required by the Meaningful Use standards include socialhistory facts, allergy facts, diagnostic test result facts, medicationfacts, problem facts, procedure facts, and vital sign facts. However,these are merely exemplary, as aspects of the invention are not limitedto any particular set of fact categories. Some embodiments may not useone or more of the above-listed fact categories, and some embodimentsmay use any other suitable fact categories. Other non-limiting examplesof suitable categories of medical facts include findings, disorders,body sites, medical devices, subdivided categories such as observablefindings and measurable findings, etc. The fact extraction component maybe implemented in any suitable form, as aspects of the present inventionare not limited in this respect. Exemplary implementations for a factextraction component are described in detail below.

Some embodiments described herein may make use of a clinical languageunderstanding (CLU) system, an exemplary operating environment for whichis illustrated in FIG. 1. CLU system 100, illustrated in FIG. 1, may beimplemented in any suitable form, as aspects of the present inventionare not limited in this respect. For example, system 100 may beimplemented as a single stand-alone machine, or may be implemented bymultiple distributed machines that share processing tasks in anysuitable manner. System 100 may be implemented as one or more computers;an example of a suitable computer is described below. In someembodiments, system 100 may include one or more tangible, non-transitorycomputer-readable storage devices storing processor-executableinstructions, and one or more processors that execute theprocessor-executable instructions to perform the functions describedherein. The storage devices may be implemented as computer-readablestorage media encoded with the processor-executable instructions;examples of suitable computer-readable storage media are discussedbelow.

As depicted, exemplary system 100 includes an ASR engine 102, a factextraction component 104, and a fact review component 106. Each of theseprocessing components of system 100 may be implemented in software,hardware, or a combination of software and hardware. Componentsimplemented in software may comprise sets of processor-executableinstructions that may be executed by the one or more processors ofsystem 100 to perform the functionality described herein. Each of ASRengine 102, fact extraction component 104 and fact review component 106may be implemented as a separate component of system 100, or anycombination of these components may be integrated into a singlecomponent or a set of distributed components. In addition, any one ofASR engine 102, fact extraction component 104 and fact review component106 may be implemented as a set of multiple software and/or hardwarecomponents. It should be understood that any such component depicted inFIG. 1 is not limited to any particular software and/or hardwareimplementation and/or configuration. Also, not all components ofexemplary system 100 illustrated in FIG. 1 are required in allembodiments. For example, in some embodiments, a CLU system may includefunctionality of fact extraction component 104, which may be implementedusing a natural language understanding (NLU) engine, without includingASR engine 102 and/or fact review component 106.

As illustrated in FIG. 1, user interface 110 is presented to a clinician120, who may be a physician, a physician's aide, a nurse, or any otherpersonnel involved in the evaluation and/or treatment of a patient 122in a clinical setting. During the course of a clinical encounter withpatient 122, or at some point thereafter, clinician 120 may wish todocument the patient encounter. Such a patient encounter may include anyinteraction between clinician 120 and patient 122 in a clinicalevaluation and/or treatment setting, including, but not limited to, anoffice visit, an interaction during hospital rounds, an outpatient orinpatient procedure (surgical or non-surgical), a follow-up evaluation,a visit for laboratory or radiology testing, etc. One method thatclinician 120 may use to document the patient encounter may be to entermedical facts that can be ascertained from the patient encounter intouser interface 110 as discrete structured data items. The set of medicalfacts, once entered, may be transmitted in some embodiments via anysuitable communication medium or media (e.g., local and/or networkconnection(s) that may include wired and/or wireless connection(s)) tosystem 100. Specifically, in some embodiments, the set of medical factsmay be received at system 100 by a fact review component 106, exemplaryfunctions of which are described below.

Another method that may be used by clinician 120 to document the patientencounter is to provide a free-form narration of the patient encounter.In some embodiments, the narration may be free-form in the sense thatclinician 120 may be unconstrained with regard to the structure andcontent of the narration, and may be free to provide any sequence ofwords, sentences, paragraphs, sections, etc., that he would like. Insome embodiments, there may be no limitation on the length of thefree-form narration, or the length may be limited only by the processingcapabilities of the user interface into which it is entered or of thelater processing components that will operate upon it. In otherembodiments, the free-form narration may be constrained in length (e.g.,limited to a particular number of characters).

A free-form narration of the patient encounter may be provided byclinician 120 in any of various ways. One way may be to manually enterthe free-form narration in textual form into user interface 110, e.g.,using a keyboard. In this respect, the one or more processors of system100 and/or of a client device in communication with system 100 may insome embodiments be programmed to present a user interface including atext editor/word processor to clinician 120. Such a text editor/wordprocessor may be implemented in any suitable way, as aspects of thepresent invention are not limited in this respect.

Another way to provide a free-form narration of the patient encountermay be to verbally speak a dictation of the patient encounter. Such aspoken dictation may be provided in any suitable way, as aspects of thepresent invention are not limited in this respect. As illustrated inFIG. 1, one way that clinician 120 may provide a spoken dictation of thefree-form narration may be to speak the dictation into a microphone 112providing input (e.g., via a direct wired connection, a direct wirelessconnection, or via a connection through an intermediate device) to userinterface 110. An audio recording of the spoken dictation may then bestored in any suitable data format, and transmitted to system 100 and/orto medical transcriptionist 130. Another way that clinician 120 mayprovide the spoken dictation may be to speak into a telephone 118, fromwhich an audio signal may be transmitted to be recorded at system 100,at the site of medical transcriptionist 130, or at any other suitablelocation. Alternatively, the audio signal may be recorded in anysuitable data format at an intermediate facility, and the audio data maythen be relayed to system 100 and/or to medical transcriptionist 130.

In some embodiments, medical transcriptionist 130 may receive the audiorecording of the dictation provided by clinician 120, and may transcribeit into a textual representation of the free-form narration (e.g., intoa text narrative). Medical transcriptionist 130 may be any human wholistens to the audio dictation and writes or types what was spoken intoa text document. In some embodiments, medical transcriptionist 130 maybe specifically trained in the field of medical transcription, and maybe well-versed in medical terminology. In some embodiments, medicaltranscriptionist 130 may transcribe exactly what she hears in the audiodictation, while in other embodiments, medical transcriptionist 130 mayadd formatting to the text transcription to comply with generallyaccepted medical document standards. When medical transcriptionist 130has completed the transcription of the free-form narration into atextual representation, the resulting text narrative may in someembodiments be transmitted to system 100 or any other suitable location(e.g., to a storage location accessible to system 100). Specifically, insome embodiments the text narrative may be received from medicaltranscriptionist 130 by fact extraction component 104 within system 100.Exemplary functionality of fact extraction component 104 is describedbelow.

In some other embodiments, the audio recording of the spoken dictationmay be received, at system 100 or any other suitable location, byautomatic speech recognition (ASR) engine 102. In some embodiments, ASRengine 102 may then process the audio recording to determine what wasspoken. As discussed above, such processing may involve any suitablespeech recognition technique, as aspects of the present invention arenot limited in this respect. In some embodiments, the audio recordingmay be automatically converted to a textual representation, while inother embodiments, words identified directly from the audio recordingmay be represented in a data format other than text, or abstractconcepts may be identified instead of words. Examples of furtherprocessing are described below with reference to a text narrative thatis a textual representation of the free-form narration; however, itshould be appreciated that similar processing may be performed on otherrepresentations of the free-form narration as discussed above. When atextual representation is produced, in some embodiments it may bereviewed by a human (e.g., a transcriptionist) for accuracy, while inother embodiments the output of ASR engine 102 may be accepted asaccurate without human review. As discussed above, some embodiments arenot limited to any particular method for transcribing audio data; anaudio recording of a spoken dictation may be transcribed manually by ahuman transcriptionist, automatically by ASR, or semiautomatically byhuman editing of a draft transcription produced by ASR. Transcriptionsproduced by ASR engine 102 and/or by transcriptionist 130 may be encodedor otherwise represented as data in any suitable form, as aspects of theinvention are not limited in this respect.

In some embodiments, ASR engine 102 may make use of a lexicon of medicalterms (which may be part of, or in addition to, another more generalspeech recognition lexicon) while determining the sequence of words thatwere spoken in the free-form narration provided by clinician 120.However, aspects of the invention are not limited to the use of alexicon, or any particular type of lexicon, for ASR. When used, themedical lexicon in some embodiments may be linked to a knowledgerepresentation model such as a clinical language understanding ontologyutilized by fact extraction component 104, such that ASR engine 102might produce a text narrative containing terms in a form understandableto fact extraction component 104. In some embodiments, a more generalspeech recognition lexicon might also be shared between ASR engine 102and fact extraction component 104. However, in other embodiments, ASRengine 102 may not have any lexicon developed to be in common with factextraction component 104. In some embodiments, a lexicon used by ASRengine 102 may be linked to a different type of medical knowledgerepresentation model, such as one not designed or used for languageunderstanding. It should be appreciated that any lexicon used by ASRengine 102 and/or fact extraction component 104 may be implementedand/or represented as data in any suitable way, as aspects of theinvention are not limited in this respect.

In some embodiments, a text narrative, whether produced by ASR engine102 (and optionally verified or not by a human), produced by medicaltranscriptionist 130, directly entered in textual form through userinterface 110, or produced in any other way, may be re-formatted in oneor more ways before being received by fact extraction component 104.Such re-formatting may be performed by ASR engine 102, by a component offact extraction component 104, by a combination of ASR engine 102 andfact extraction component 104, or by any other suitable software and/orhardware component. In some embodiments, the re-formatting may beperformed in a way known to facilitate fact extraction, and may beperformed for the purpose of facilitating the extraction of clinicalfacts from the text narrative by fact extraction component 104. Forexample, in some embodiments, processing to perform fact extraction maybe improved if sentence boundaries in the text narrative are accurate.Accordingly, in some embodiments, the text narrative may be re-formattedprior to fact extraction to add, remove or correct one or more sentenceboundaries within the text narrative. In some embodiments, this mayinvolve altering the punctuation in at least one location within thetext narrative. In another example, fact extraction may be improved ifthe text narrative is organized into sections with headings, and thusthe re-formatting may include determining one or more section boundariesin the text narrative and adding, removing or correcting one or morecorresponding section headings. In some embodiments, the re-formattingmay include normalizing one or more section headings (which may havebeen present in the original text narrative and/or added or corrected aspart of the re-formatting) according to a standard for the healthcareinstitution corresponding to the patient encounter (which may be aninstitution-specific standard or a more general standard for sectionheadings in clinical documents). In some embodiments, a user (such asclinician 120, medical transcriptionist 130, or another user) may beprompted to approve the re-formatted text.

In some embodiments, either an original or a re-formatted text narrativemay be received by fact extraction component 104, which may performprocessing to extract one or more medical facts (e.g., clinical facts)from the text narrative. The text narrative may be received from ASRengine 102, from medical transcriptionist 130, directly from clinician120 via user interface 110, or in any other suitable way. Any suitabletechnique(s) for extracting facts from the text narrative may be used,as aspects of the present invention are not limited in this respect.Exemplary techniques for medical fact extraction are described below.

In some embodiments, a fact extraction component may be implementedusing techniques such as those described in U.S. Pat. No. 7,493,253,entitled “Conceptual World Representation Natural Language UnderstandingSystem and Method.” U.S. Pat. No. 7,493,253 is incorporated herein byreference in its entirety. Such a fact extraction component may make useof a formal ontology linked to a lexicon of clinical terms. The formalontology may be implemented as a relational database, or in any othersuitable form, and may represent semantic concepts relevant to themedical domain, as well as linguistic concepts related to ways thesemantic concepts may be expressed in natural language.

In some embodiments, concepts in a formal ontology used by a factextraction component may be linked to a lexicon of medical terms and/orcodes, such that each medical term and each code is linked to at leastone concept in the formal ontology. In some embodiments, the lexicon mayinclude the standard medical terms and/or codes used by the institutionin which the fact extraction component is applied. For example, thestandard medical terms and/or codes used by an EHR maintained by theinstitution may be included in the lexicon linked to the fact extractioncomponent's formal ontology. In some embodiments, the lexicon may alsoinclude additional medical terms used by the various clinicians withinthe institution, and/or used by clinicians generally, when describingmedical issues in a free-form narration. Such additional medical termsmay be linked, along with their corresponding standard medical terms, tothe appropriate shared concepts within the formal ontology. For example,the standard term “acute myocardial infarction” as well as othercorresponding terms such as “heart attack”, “acute MI” and “AMI” may allbe linked to the same abstract concept in the formal ontology—a conceptrepresenting an interruption of blood supply to the heart. Such linkageof multiple medical terms to the same abstract concept in someembodiments may relieve the clinician of the burden of ensuring thatonly standard medical terms preferred by the institution appear in thefree-form narration. For example, in some embodiments, a clinician maybe free to use the abbreviation “AMI” or the colloquial “heart attack”in his free-form narration, and the shared concept linkage may allow thefact extraction component to nevertheless automatically extract a factcorresponding to “acute myocardial infarction”.

In some embodiments, a formal ontology used by a fact extractioncomponent may also represent various types of relationships between theconcepts represented. One type of relationship between two concepts maybe a parent-child relationship, in which the child concept is a morespecific version of the parent concept. More formally, in a parent-childrelationship, the child concept inherits all necessary properties of theparent concept, while the child concept may have necessary propertiesthat are not shared by the parent concept. For example, “heart failure”may be a parent concept, and “congestive heart failure” may be a childconcept of “heart failure.” In some embodiments, any other type(s) ofrelationship useful to the process of medical documentation may also berepresented in the formal ontology. For example, one type ofrelationship may be a symptom relationship. In one example of a symptomrelationship, a concept linked to the term “chest pain” may have arelationship of “is-symptom-of” to the concept linked to the term “heartattack”. Other types of relationships may include complicationrelationships, comorbidity relationships, interaction relationships(e.g., among medications), and many others. Any number and type(s) ofconcept relationships may be included in such a formal ontology, asaspects of the present invention are not limited in this respect.

In some embodiments, automatic extraction of medical facts from aclinician's free-form narration may involve parsing the free-formnarration to identify medical terms that are represented in the lexiconof the fact extraction component. Concepts in the formal ontology linkedto the medical terms that appear in the free-form narration may then beidentified, and concept relationships in the formal ontology may betraced to identify further relevant concepts. Through theserelationships, as well as the linguistic knowledge represented in theformal ontology, one or more medical facts may be extracted. Forexample, if the free-form narration includes the medical term“hypertension” and the linguistic context relates to the patient's past,the fact extraction component may automatically extract a factindicating that the patient has a history of hypertension. On the otherhand, if the free-form narration includes the medical term“hypertension” in a sentence about the patient's mother, the factextraction component may automatically extract a fact indicating thatthe patient has a family history of hypertension. In some embodiments,relationships between concepts in the formal ontology may also allow thefact extraction component to automatically extract facts containingmedical terms that were not explicitly included in the free-formnarration. For example, the medical term “meningitis” can also bedescribed as inflammation in the brain. If the free-form narrationincludes the terms “inflammation” and “brain” in proximity to eachother, then relationships in the formal ontology between concepts linkedto the terms “inflammation”, “brain” and “meningitis” may allow the factextraction component to automatically extract a fact corresponding to“meningitis”, despite the fact that the term “meningitis” was not statedin the free-form narration.

It should be appreciated that the foregoing descriptions are provided byway of example only, and that any suitable technique(s) for extracting aset of one or more medical facts from a free-form narration may be used,as aspects of the present invention are not limited to any particularfact extraction technique. For instance, it should be appreciated thatfact extraction component 104 is not limited to the use of an ontology,as other forms of knowledge representation models, including statisticalmodels and/or rule-based models, may also be used. The knowledgerepresentation model may also be represented as data in any suitableformat, and may be stored in any suitable location, such as in a storagemedium of system 100 accessible by fact extraction component 104, asaspects of the invention are not limited in this respect. In addition, aknowledge representation model such as an ontology used by factextraction component 104 may be constructed in any suitable way, asaspects of the invention are not limited in this respect.

For instance, in some embodiments a knowledge representation model maybe constructed manually by one or more human developers with access toexpert knowledge about medical facts, diagnoses, problems, potentialcomplications, comorbidities, appropriate observations and/or clinicalfindings, and/or any other relevant information. In other embodiments, aknowledge representation model may be generated automatically, forexample through statistical analysis of past medical reports documentingpatient encounters, of medical literature and/or of other medicaldocuments. Thus, in some embodiments, fact extraction component 104 mayhave access to a data set 170 of medical literature and/or otherdocuments such as past patient encounter reports. In some embodiments,past reports and/or other text documents may be marked up (e.g., by ahuman) with labels indicating the nature of the relevance of particularstatements in the text to the patient encounter or medical topic towhich the text relates. A statistical knowledge representation model maythen be trained to form associations based on the prevalence ofparticular labels corresponding to similar text within an aggregate setof multiple marked up documents. For example, if “pneumothorax” islabeled as a “complication” in a large enough proportion of clinicalprocedure reports documenting pacemaker implantation procedures, astatistical knowledge representation model may generate and store aconcept relationship that “pneumothorax is-complication-of pacemakerimplantation.” In some embodiments, automatically generated and hardcoded (e.g., by a human developer) concepts and/or relationships mayboth be included in a knowledge representation model used by factextraction component 104.

As discussed above, it should be appreciated that aspects of theinvention are not limited to any particular technique(s) forconstructing knowledge representation models. Examples of suitabletechniques include those disclosed in the following:

-   Gómez-Pérez, A., and Manzano-Macho, D. (2005). An overview of    methods and tools for ontology learning from texts. Knowledge    Engineering Review 19, p. 187-212.-   Cimiano, P., and Staab, S. (2005). Learning concept hierarchies from    text with a guided hierarchical clustering algorithm. In C. Biemann    and G. Paas (eds.), Proceedings of the ICML 2005 Workshop on    Learning and Extending Lexical Ontologies with Machine Learning    Methods, Bonn, Germany.-   Fan, J., Ferrucci, D., Gondek, D., and Kalyanpur, A. (2010).    PRISMATIC: Inducing Knowledge from a Lange Scale Lexicalized    Relation Resource. NAACL Workshop on Formalisms and Methodology for    Learning by Reading.-   Welty, C., Fan, J., Gondek, D. and Schlaikjer, A. (2010). Large    scale relation detection. NAACL Workshop on Formalisms and    Methodology for Learning by Reading.

Each of the foregoing publications is incorporated herein by referencein its entirety.

Alternatively or additionally, in some embodiments a fact extractioncomponent may make use of one or more statistical models to extractsemantic entities from natural language input. In general, a statisticalmodel can be described as a functional component designed and/or trainedto analyze new inputs based on probabilistic patterns observed in priortraining inputs. In this sense, statistical models differ from“rule-based” models, which typically apply hard-coded deterministicrules to map from inputs having particular characteristics to particularoutputs. By contrast, a statistical model may operate to determine aparticular output for an input with particular characteristics byconsidering how often (e.g., with what probability) training inputs withthose same characteristics (or similar characteristics) were associatedwith that particular output in the statistical model's training data. Tosupply the probabilistic data that allows a statistical model toextrapolate from the tendency of particular input characteristics to beassociated with particular outputs in past examples, statistical modelsare typically trained (or “built”) on large training corpuses with greatnumbers of example inputs. Typically the example inputs are labeled withthe known outputs with which they should be associated, usually by ahuman labeler with expert knowledge of the domain. Characteristics ofinterest (known as “features”) are identified (“extracted”) from theinputs, and the statistical model learns the probabilities with whichdifferent features are associated with different outputs, based on howoften training inputs with those features are associated with thoseoutputs. When the same features are extracted from a new input (e.g., aninput that has not been labeled with a known output by a human), thestatistical model can then use the learned probabilities for theextracted features (as learned from the training data) to determinewhich output is most likely correct for the new input. Exemplaryimplementations of a fact extraction component using one or morestatistical models are described further below.

In some embodiments, fact extraction component 104 may utilize astatistical fact extraction model based on entity detection and/ortracking techniques, such as those disclosed in: Florian, R., Hassan,H., Ittycheriah, A., Jing, H., Kambhatla, N., Luo, X., Nicolov, N., andRoukos, S. (2004). A Statistical Model for Multilingual Entity Detectionand Tracking. Proceedings of the Human Language Technologies Conference2004 (HLT-NAACL′04). This publication is incorporated herein byreference in its entirety.

For example, in some embodiments, a list of fact types of interest forgenerating medical reports may be defined, e.g., by a developer of factextraction component 104. Such fact types (also referred to herein as“entity types”) may include, for example, problems, disorders (adisorder is a type of problem), diagnoses (a diagnosis may be a disorderthat a clinician has identified as a problem for a particular patient),findings (a finding is a type of problem that need not be a disorder),medications, body sites, social history facts, allergies, diagnostictest results, vital signs, procedures, procedure steps, observations,devices, and/or any other suitable medical fact types. It should beappreciated that any suitable list of fact types may be utilized, andmay or may not include any of the fact types listed above, as aspects ofthe invention are not limited in this respect. In some embodiments,spans of text in a set of sample patient encounter reports may belabeled (e.g., by a human) with appropriate fact types from the list. Astatistical model may then be trained on the corpus of labeled samplereports to detect and/or track such fact types as semantic entities,using entity detection and/or tracking techniques, examples of which aredescribed below.

For example, in some embodiments, a large number of past free-formnarrations created by clinicians may be manually labeled to form acorpus of training data for a statistical entity detection model. Asdiscussed above, in some embodiments, a list of suitable entities may bedefined (e.g., by a domain administrator) to include medical fact typesthat are to be extracted from future clinician narrations. One or morehuman labelers (e.g., who may have specific knowledge about medicalinformation and typical clinician narration content) may then manuallylabel portions of the training texts with the particular definedentities to which they correspond. For example, given the training text,“Patient is complaining of acute sinusitis,” a human labeler may labelthe text portion “acute sinusitis” with the entity label “Problem.” Inanother example, given the training text, “He has sinusitis, whichappears to be chronic,” a human labeler may label the text “sinusitis”and “chronic” with a single label indicating that both words togethercorrespond to a “Problem” entity. As should be clear from theseexamples, the portion of the text labeled as corresponding to a singleconceptual entity need not be formed of contiguous words, but may havewords split up within the text, having non-entity words in between.

In some embodiments, the labeled corpus of training data may then beprocessed to build a statistical model trained to detect mentions of theentities labeled in the training data. Each time the same conceptualentity appears in a text, that appearance is referred to as a mention ofthat entity. For example, consider the text, “Patient has sinusitis. Hissinusitis appears to be chronic.” In this example, the entity detectionmodel may be trained to identify each appearance of the word “sinusitis”in the text as a separate mention of the same “Problem” entity.

In some embodiments, the process of training a statistical entitydetection model on labeled training data may involve a number of stepsto analyze each training text and probabilistically associate itscharacteristics with the corresponding entity labels. In someembodiments, each training text (e.g., free-form clinician narration)may be tokenized to break it down into various levels of syntacticsubstructure. For example, in some embodiments, a tokenizer module maybe implemented to designate spans of the text as representingstructural/syntactic units such as document sections, paragraphs,sentences, clauses, phrases, individual tokens, words, sub-word unitssuch as affixes, etc. In some embodiments, individual tokens may oftenbe single words, but some tokens may include a sequence of more than oneword that is defined, e.g., in a dictionary, as a token. For example,the term “myocardial infarction” could be defined as a token, althoughit is a sequence of more than one word. In some embodiments, a token'sidentity (i.e., the word or sequence of words itself) may be used as afeature of that token. In some embodiments, the token's placement withinparticular syntactic units in the text (e.g., its section, paragraph,sentence, etc.) may also be used as features of the token.

In some embodiments, an individual token within the training text may beanalyzed (e.g., in the context of the surrounding sentence) to determineits part of speech (e.g., noun, verb, adjective, adverb, preposition,etc.), and the token's part of speech may be used as a further featureof that token. In some embodiments, each token may be tagged with itspart of speech, while in other embodiments, not every token may betagged with a part of speech. In some embodiments, a list of relevantparts of speech may be pre-defined, e.g., by a developer of thestatistical model, and any token having a part of speech listed asrelevant may be tagged with that part of speech. In some embodiments, aparser module may be implemented to determine the syntactic structure ofsentences in the text, and to designate positions within the sentencestructure as features of individual tokens. For example, in someembodiments, the fact that a token is part of a noun phrase or a verbphrase may be used as a feature of that token. Any type of parser may beused, non-limiting examples of which include a bottom-up parser and/or adependency parser, as aspects of the invention are not limited in thisrespect. In some embodiments, section membership may be used as afeature of a token.

In some embodiments, a section normalization module may be implementedto associate various portions of the narrative text with the propersection to which it should belong. In some embodiments, a set ofstandardized section types (e.g., identified by their section headings)may be defined for all texts, or a different set of normalized sectionheadings may be defined for each of a number of different types of texts(e.g., corresponding to different types of documents). For example, insome embodiments, a different set of normalized section headings may bedefined for each type of medical document in a defined set of medicaldocument types. Non-limiting examples of medical document types includeconsultation reports, history & physical reports, discharge summaries,and emergency room reports, although there are also many other examples.In the medical field, the various types of medical documents are oftenreferred to as “work types.” In some cases, the standard set of sectionsfor various types of medical documents may be established by a suitablesystem standard, institutional standard, or more widely applicablestandard, such as the Meaningful Use standard (discussed above) or theLogical Observation Identifiers Names and Codes (LOINC) standardmaintained by the Regenstrief Institute. For example, an expected set ofsection headings for a history & physical report under the MeaningfulUse standard may include headings for a “Reason for Visit” section, a“History of Present Illness” section, a “History of Medication Use”section, an “Allergies, Adverse Reactions and Alerts” section, a “Reviewof Systems” section, a “Social History” section, a “Physical Findings”section, an “Assessment and Plan” section, and/or any other suitablesection(s). Any suitable set of sections may be used, however, asaspects of the invention are not limited in this respect.

A section normalization module may use any suitable technique toassociate portions of text with normalized document sections, as aspectsof the invention are not limited in this respect. In some embodiments,the section normalization module may use a table (e.g., stored as datain a storage medium) to map text phrases that commonly occur in medicaldocuments to the sections to which they should belong. In anotherexample, a statistical model may be trained to determine the most likelysection for a portion of text based on its semantic content, thesemantic content of surrounding text portions, and/or the expectedsemantic content of the set of normalized sections. In some embodiments,once a normalized section for a portion of text has been identified, themembership in that section may be used as a feature of one or moretokens in that portion of text.

In some embodiments, other types of features may be extracted, i.e.,identified and associated with tokens in the training text. For example,in some embodiments, an N-gram feature may identify the previous (N−1)words and/or tokens in the text as a feature of the current token. Inanother example, affixes (e.g., suffixes such as -ectomy, -oma, -itis,etc.) may be used as features of tokens. In another example, one or morepredefined dictionaries and/or ontologies may be accessed, and a token'smembership in any of those dictionaries may be used as a feature of thattoken. For example, a predefined dictionary of surgical procedures maybe accessed, and/or a dictionary of body sites, and/or a dictionary ofknown diseases, etc. It should be appreciated, however, that all of theforegoing feature types are merely examples, and any suitable numberand/or types of features of interest may be designated, e.g., by adeveloper of the statistical entity detection model, as aspects of theinvention are not limited in this respect.

In some embodiments, the corpus of training text with its hand-labeledfact type entity labels, along with the collection of features extractedfor tokens in the text, may be input to the statistical entity detectionmodel for training. As discussed above, examples of suitable featuresinclude position within document structure, syntactic structure, partsof speech, parser features, N-gram features, affixes (e.g., prefixesand/or suffixes), membership in dictionaries (sometimes referred to as“gazetteers”) and/or ontologies, surrounding token contexts (e.g., acertain number of tokens to the left and/or right of the current token),orthographic features (e.g., capitalization, letters vs. numbers, etc.),entity labels assigned to previous tokens in the text, etc. As onenon-limiting example, consider the training sentence, “Patient iscomplaining of acute sinusitis,” for which the word sequence “acutesinusitis” was hand-labeled as being a “Problem” entity. In oneexemplary implementation, features extracted for the token “sinusitis”may include the token identity feature that the word is “sinusitis,” asyntactic feature specifying that the token occurred at the end of asentence (e.g., followed by a period), a part-of-speech feature of“noun,” a parser feature that the token is part of a noun phrase (“acutesinusitis”), a trigram feature that the two preceding words are “ofacute,” an affix feature of “-itis,” and a dictionary feature that thetoken is a member of a predefined dictionary of types of inflammation.It should be appreciated, however, that the foregoing list of featuresis merely exemplary, as any suitable features may be used. Aspects ofthe invention are not limited to any of the features listed above, andimplementations including some, all, or none of the above features, aswell as implementations including features not listed above, arepossible.

In some embodiments, given the extracted features and manual entitylabels for the entire training corpus as input, the statistical entitydetection model may be trained to be able to probabilistically label newtexts (e.g., texts not included in the training corpus) with automaticentity labels using the same feature extraction technique that wasapplied to the training corpus. In other words, by processing the inputfeatures and manual entity labels of the training corpus, thestatistical model may learn probabilistic relationships between thefeatures and the entity labels. When later presented with an input textwithout manual entity labels, the statistical model may then apply thesame feature extraction techniques to extract features from the inputtext, and may apply the learned probabilistic relationships toautomatically determine the most likely entity labels for word sequencesin the input text. Any suitable statistical modeling technique may beused to learn such probabilistic relationships, as aspects of theinvention are not limited in this respect. Non-limiting examples ofsuitable known statistical modeling techniques include machine learningtechniques such as maximum entropy modeling, support vector machines,and conditional random fields, among others.

In some embodiments, training the statistical entity detection model mayinvolve learning, for each extracted feature, a probability with whichtokens having that feature are associated with each entity type. Forexample, for the suffix feature “-itis,” the trained statistical entitydetection model may store a probability p1 that a token with thatfeature should be labeled as being part of a “Problem” entity, aprobability p2 that a token with that feature should be labeled as beingpart of a “Medication” entity, etc. In some embodiments, suchprobabilities may be learned by determining the frequency with whichtokens having the “-itis” feature were hand-labeled with each differententity label in the training corpus. In some embodiments, theprobabilities may be normalized such that, for each feature, theprobabilities of being associated with each possible entity (fact type)may sum to 1. However, aspects of the invention are not limited to suchnormalization. In some embodiments, each feature may also have aprobability p0 of not being associated with any fact type, such that thenon-entity probability p0 plus the probabilities of being associatedwith each possible fact type sum to 1 for a given feature. In otherembodiments, separate classifiers may be trained for each fact type, andthe classifiers may be run in parallel. For example, the “-itis” featuremay have probability p1 of being part of a “Problem” entity andprobability (1-p1) of not being part of a “Problem” entity, probabilityp2 of being part of a “Medication” entity and probability (1-p2) of notbeing part of a “Medication” entity, and so on. In some embodiments,training separate classifiers may allow some word sequences to have anon-zero probability of being labeled with more than one fact typesimultaneously; for example, “kidney failure” could be labeled asrepresenting both a Body Site and a Problem. In some embodiments,classifiers may be trained to identify sub-portions of an entity label.For example, the feature “-itis” could have a probability p_(B) of itstoken being at the beginning of a “Problem” entity label, a probabilityp_(I) of its token being inside a “Problem” entity label (but not at thebeginning of the label), and a probability p_(O) of its token beingoutside a “Problem” entity label (i.e., of its token not being part of a“Problem” entity).

In some embodiments, the statistical entity detection model may befurther trained to weight the individual features of a token todetermine an overall probability that it should be associated with aparticular entity label. For example, if the token “sinusitis” has nextracted features f1 . . . fn having respective probabilities p1 . . .pn of being associated with a “Problem” entity label, the statisticalmodel may be trained to apply respective weights w1 . . . wn to thefeature probabilities, and then combine the weighted featureprobabilities in any suitable way to determine the overall probabilitythat “sinusitis” should be part of a “Problem” entity. Any suitabletechnique for determining such weights may be used, including knownmodeling techniques such as maximum entropy modeling, support vectormachines, conditional random fields, and/or others, as aspects of theinvention are not limited in this respect.

In some embodiments, when an unlabeled text is input to the trainedstatistical entity detection model, the model may process the text toextract features and determine probabilities for individual tokens ofbeing associated with various entity (e.g., fact type) labels. In someembodiments, the most probable label (including the non-entity label, ifit is most probable) may be selected for each token in the input text.In other embodiments, labels may be selected through more contextualanalysis, such as at the phrase level or sentence level, rather than atthe token level. Any suitable technique, such as Viterbi techniques, orany other suitable technique, may be used, as aspects of the inventionare not limited in this respect. In some embodiments, a lattice may beconstructed of the associated probabilities for all entity types for alltokens in a sentence, and the best (e.g., highest combined probability)path through the lattice may be selected to determine which wordsequences in the sentence are to be automatically labeled with whichentity (e.g., fact type) labels. In some embodiments, not only the bestpath may be identified, but also the (N−1)-best alternative paths withthe next highest associated probabilities. In some embodiments, this mayresult in an N-best list of alternative hypotheses for fact type labelsto be associated with the same input text.

In some embodiments, a statistical model may also be trained toassociate fact types extracted from new reports with particular facts tobe extracted from those reports (e.g., to determine a particular conceptrepresented by the text portion that has been labeled as an entitymention). For example, in some embodiments, a statistical factextraction model may be applied to automatically label “acute sinusitis”not only with the “Problem” entity (fact type) label, but also with alabel indicating the particular medical fact (e.g., concept) indicatedby the word sequence (e.g., the medical fact “sinusitis, acute”). Insuch embodiments, for example, a single statistical model may be trainedto detect specific particular facts as individual entities. For example,in some embodiments, the corpus of training text may be manually labeledby one or more human annotators with labels indicating specific medicalfacts, rather than labels indicating more general entities such as facttypes or categories. However, in other embodiments, the process ofdetecting fact types as entities may be separated from the process ofrelating detected fact types to particular facts. For example, in someembodiments, a separate statistical model (e.g., an entity detectionmodel) may be trained to automatically label portions of text with facttype labels, and another separate statistical model (e.g., a relationmodel) may be trained to identify which labeled entity (fact type)mentions together indicate a single specific medical fact. In somecases, the relation model may identify particular medical facts byrelating together two or more mentions labeled with the same entitytype.

For example, in the text, “Patient is complaining of acute sinusitis,”in some embodiments an entity detection model may label the tokens“acute” and “sinusitis” as being part of a “Problem” entity. In someembodiments, a relation model, given that “acute” and “sinusitis” havebeen labeled as “Problem,” may then relate the two tokens together to asingle medical fact of “sinusitis, acute.” For another example, considerthe text, “Patient has sinusitis, which appears to be chronic.” In someembodiments, an entity detection model may be applied to label thetokens “sinusitis” and “chronic” as “Problem” entity mentions. In someembodiments, a relation model may then be applied to determine that thetwo “Problem” entity mentions “sinusitis” and “chronic” are related(even though they are not contiguous in the text) to represent a singlemedical fact of “sinusitis, chronic.” For yet another example, considerthe text, “She has acute sinusitis; chronic attacks of asthma may be afactor.” In some embodiments, an entity detection model may label eachof the tokens “acute,” “sinusitis,” “chronic,” and “asthma” as belongingto “Problem” entity mentions. In some embodiments, a relation model maythen be applied to determine which mentions relate to the same medicalfact. For example, the relation model may determine that the tokens“acute” and “sinusitis” relate to a first medical fact (e.g.,“sinusitis, acute”), while the tokens “chronic” and “asthma” relate to adifferent medical fact (e.g., “asthma, chronic”), even though the token“chronic” is closer in the sentence to the token “sinusitis” than to thetoken “asthma.”

In some embodiments, a relation model may be trained statistically usingmethods similar to those described above for training the statisticalentity detection model. For example, in some embodiments, training textsmay be manually labeled with various types of relations between entitymentions and/or tokens within entity mentions. For example, in thetraining text, “Patient has sinusitis, which appears to be chronic,” ahuman annotator may label the “Problem” mention “chronic” as having arelation to the “Problem” mention “sinusitis,” since both mentions referto the same medical fact. In some embodiments, the relation annotationsmay simply indicate that certain mentions are related to each other,without specifying any particular type of relationship. In otherembodiments, relation annotations may also indicate specific types ofrelations between entity mentions. Any suitable number and/or types ofrelation annotations may be used, as aspects of the invention are notlimited in this respect. For example, in some embodiments, one type ofrelation annotation may be a “split” relation label. The tokens“sinusitis” and “chronic,” for example, may be labeled as having a splitrelationship, because “sinusitis” and “chronic” together make up anentity, even though they are not contiguous within the text. In thiscase, “sinusitis” and “chronic” together indicate a specific type ofsinusitis fact, i.e., one that it is chronic and not, e.g., acute.Another exemplary type of relation may be an “attribute” relation. Insome embodiments, one or more system developers may define sets ofattributes for particular fact types, corresponding to relatedinformation that may be specified for a fact type. For example, a“Medication” fact type may have attributes “dosage,” “route,”“frequency,” “duration,” etc. In another example, an “Allergy” fact typemay have attributes “allergen,” “reaction,” “severity,” etc. It shouldbe appreciated, however, that the foregoing are merely examples, andthat aspects of the invention are not limited to any particularattributes for any particular fact types. Also, other types of factrelations are possible, including family relative relations,causes-problem relations, improves-problem relations, and many others.Aspects of the invention are not limited to use of any particularrelation types.

In some embodiments, using techniques similar to those described above,the labeled training text may be used as input to train the statisticalrelation model by extracting features from the text, andprobabilistically associating the extracted features with the manuallysupplied labels. Any suitable set of features may be used, as aspects ofthe invention are not limited in this respect. For example, in someembodiments, features used by a statistical relation model may includeentity (e.g., fact type) labels, parts of speech, parser features,N-gram features, token window size (e.g., a count of the number of wordsor tokens present between two tokens that are being related to eachother), and/or any other suitable features. It should be appreciated,however, that the foregoing features are merely exemplary, asembodiments are not limited to any particular list of features. In someembodiments, rather than outputting only the best (e.g., most probable)hypothesis for relations between entity mentions, a statistical relationmodel may output a list of multiple alternative hypotheses, e.g., withcorresponding probabilities, of how the entity mentions labeled in theinput text are related to each other. In yet other embodiments, arelation model may be hard-coded and/or otherwise rule-based, while theentity detection model used to label text portions with fact types maybe trained statistically.

In some embodiments, the relation model or another statistical model mayalso be trained to track mentions of the same entity from differentsentences and/or document sections and to relate them together.Exemplary techniques for entity tracking are described in thepublication by Florian cited above.

In some embodiments, further processing may be applied to normalizeparticular facts extracted from the text to standard forms and/or codesin which they are to be documented. For example, medical personnel oftenhave many different ways of phrasing the same medical fact, and anormalization/coding process in some embodiments may be applied toidentify the standard form and/or code corresponding to each extractedmedical fact that was stated in a non-standard way. The standard formand/or code may be derived from any suitable source, as aspects of theinvention are not limited in this respect. Some standard terms and/orcodes may be derived from a government or profession-wide standard, suchas SNOMED (Systematized Nomenclature of Medicine), UMLS (Unified MedicalLanguage System), RxNorm, RadLex, etc. Other standard terms and/or codesmay be more locally derived, such as from standard practices of aparticular locality or institution. Still other standard terms and/orcodes may be specific to the documentation system including the factextraction component being applied.

For example, given the input text, “His sinuses are constantlyinflamed,” in some embodiments, an entity detection model together witha relation model (or a single model performing both functions) mayidentify the tokens “sinuses,” “constantly” and “inflamed” asrepresenting a medical fact. In some embodiments, a normalization/codingprocess may then be applied to identify the standard form fordocumenting “constantly inflamed sinuses” as “sinusitis, chronic.”Alternatively or additionally, in some embodiments thenormalization/coding process may identify a standard code used todocument the identified fact. For example, the ICD-9 code for“sinusitis, chronic” is ICD-9 code #473. Any suitable coding system maybe used, as aspects of the invention are not limited in this respect.Exemplary standard codes include ICD (International Classification ofDiseases) codes, CPT (Current Procedural Terminology) codes, E&M(Evaluation and Management) codes, MedDRA (Medical Dictionary forRegulatory Activities) codes, SNOMED codes, LOINC (Logical ObservationIdentifiers Names and Codes) codes, RxNorm codes, NDC (National DrugCode) codes and RadLex codes.

In some embodiments, a normalization/coding process may be rule-based(e.g., using lists of possible ways of phrasing particular medicalfacts, and/or using an ontology of medical terms and/or other languageunits to normalize facts extracted from input text to their standardforms). For example, in some embodiments, the tokens identified in thetext as corresponding to a medical fact may be matched to correspondingterms in an ontology. In some embodiments, a list of closest matchingterms may be generated, and may be ranked by their similarity to thetokens in the text. The similarity may be scored in any suitable way.For example, in one suitable technique, one or more tokens in the textmay be considered as a vector of its component elements, such as words,and each of the terms in the ontology may also be considered as a vectorof component elements such as words. Similarity scores between thetokens may then be computed by comparing the corresponding vectors,e.g., by calculating the angle between the vectors, or a relatedmeasurement such as the cosine of the angle. In some embodiments, one ormore concepts that are linked in the ontology to one or more of thehigher ranking terms (e.g., the terms most similar to the identifiedtokens in the text) may then be identified as hypotheses for the medicalfact to be extracted from that portion of the text. Exemplary techniquesthat may be used in some embodiments are described in Salton, Wong, &Yang: “A vector space model for automatic indexing,” Communications ofthe ACM, November 1975. This publication is incorporated herein byreference in its entirety. However, these are merely examples, and anysuitable technique(s) for normalizing entity tokens to standard termsmay be utilized in some embodiments, as aspects of the invention are notlimited in this respect.

In some embodiments, the normalization/coding process may output asingle hypothesis for the standard form and/or code corresponding toeach extracted fact. For example, the single output hypothesis maycorrespond to the concept linked in the ontology to the term that ismost similar to the token(s) in the text from which the fact isextracted. However, in other embodiments, the normalization/codingprocess may output multiple alternative hypotheses, e.g., withcorresponding probabilities, for the standard form and/or codecorresponding to an individual extracted fact. Thus, it should beappreciated that in some embodiments multiple alternative hypotheses fora medical fact to be extracted from a portion of input text may beidentified by fact extraction component 104. Such alternative hypothesesmay be collected at any or all of various processing levels of factextraction, including entity detection, entity relation, and/ornormalization/coding stages. In some embodiments, the list ofalternative hypotheses may be thresholded at any of the various levels,such that the final list output by fact extraction component 104 mayrepresent the N-best alternative hypotheses for a particular medicalfact to be extracted.

It should be appreciated that the foregoing are merely examples, andthat fact extraction component 104 may be implemented in any suitableway and/or form, as aspects of the invention are not limited in thisrespect.

In some embodiments, a user such as clinician 120 may monitor, controland/or otherwise interact with the fact extraction and/or fact reviewprocess through a user interface provided in connection with system 100.For example, in some embodiments, user interface 140 may be provided byfact review component 106, e.g., through execution (e.g., by one or moreprocessors of system 100) of programming instructions incorporated infact review component 106. One exemplary implementation of such a userinterface is graphical user interface (GUI) 200, illustrated in FIG. 2.In some embodiments, when the user is clinician 120, GUI 200 may bepresented via user interface 110. In some embodiments, a user may be aperson other than a clinician; for example, another person such ascoding specialist 150 may be presented with GUI 200 via user interface140. However, it should be appreciated that “user,” as used herein,refers to an end user of system 100, as opposed to a software and/orhardware developer of any component of system 100.

The user interface is not limited to a graphical user interface, asother ways of providing data from system 100 to users may be used. Forexample, in some embodiments, audio indicators may be transmitted fromsystem 100 and conveyed to a user. It should be appreciated that anytype of user interface may be provided in connection with factextraction, fact review and/or other related processes, as aspects ofthe invention are not limited in this respect. While the exemplaryembodiments illustrated in FIG. 1 involve data processing at system 100and data communication between system 100 and user interfaces 110 and/or140, it should be appreciated that in other embodiments any or allprocessing components of system 100 may instead be implemented locallyat user interface 110 and/or user interface 140, as aspects of theinvention are not limited to any particular distribution of local and/orremote processing capabilities.

As depicted in FIG. 2, GUI 200 includes a number of separate panesdisplaying different types of data. Identifying information pane 210includes general information identifying patient 222 as a male patientnamed John Doe. Such general patient identifying information may beentered by clinician 120, or by other user 150, or may be automaticallypopulated from an electronic medical record for patient 122, or may beobtained from any other suitable source. Identifying information pane210 also displays the creation date and document type of the reportcurrently being worked on. This information may also be obtained fromany suitable source, such as from stored data or by manual entry. Whenreferring herein to entry of data by clinician 120 and/or other user150, it should be appreciated that any suitable form of data entry maybe used, including input via mouse, keyboard, touchscreen, stylus,voice, or any other suitable input form, as aspects of the invention arenot limited in this respect.

GUI 200 as depicted in FIG. 2 includes a text panel 220 in which a textnarrative referring to the encounter between clinician 120 and patient122 is displayed. In some embodiments, text panel 220 may include texteditor functionality, such that clinician 120 may directly enter thetext narrative into text panel 220, either during the patient encounteror at some time thereafter. If ASR is used to produce the text narrativefrom a spoken dictation provided by clinician 120, in some embodimentsthe text may be displayed in text panel 220 as it is produced by ASRengine 102, either in real time while clinician 120 is dictating, orwith a larger processing delay. In other embodiments, the text narrativemay be received as stored data from another source, such as from medicaltranscriptionist 130, and may be displayed in completed form in textpanel 220. In some embodiments, the text narrative may then be edited ifdesired by clinician 120 and/or other user 150 within text panel 220.However, text editing capability is not required, and in someembodiments text panel 220 may simply display the text narrative withoutproviding the ability to edit it.

Exemplary GUI 200 further includes a fact panel 230 in which one or moremedical facts, once extracted from the text narrative and/or entered inanother suitable way, may be displayed as discrete structured dataitems. When clinician 120 and/or other user 150 is ready to direct factextraction component 104 to extract one or more medical facts from thetext narrative, in some embodiments he or she may select process button240 via any suitable selection input method. However, a user indicationto begin fact extraction is not limited to a button such as processbutton 240, as any suitable way to make such an indication may beprovided by GUI 200. In some embodiments, no user indication to beginfact extraction may be required, and fact extraction component 104 maybegin a fact extraction process as soon as a requisite amount of text(e.g., enough text for fact extraction component 104 to identify one ormore clinical facts that can be ascertained therefrom) is entered and/orreceived. In some embodiments, a user may select process button 240 tocause fact extraction to be performed before the text narrative iscomplete. For example, clinician 120 may dictate, enter via manual inputand/or otherwise provide a part of the text narrative, select processbutton 240 to have one or more facts extracted from that part of thetext narrative, and then continue to provide further part(s) of the textnarrative. In another example, clinician 120 may provide all or part ofthe text narrative, select process button 240 and review the resultingextracted facts, edit the text narrative within text pane 220, and thenselect process button 240 again to review how the extracted facts maychange.

In some embodiments, one or more medical facts extracted from the textnarrative by fact extraction component 104 may be displayed to the uservia GUI 200 in fact panel 230. Screenshots illustrating an exampledisplay of medical facts extracted from an example text narrative areprovided in FIGS. 3A and 3B. FIG. 3A is a screenshot with fact panel 230scrolled to the top of a display listing medical facts extracted fromthe example text narrative, and FIG. 3B is a screenshot with fact panel230 scrolled to the bottom of the display listing the extracted medicalfacts. In some embodiments, as depicted in FIGS. 3A and 3B, medicalfacts corresponding to a patient encounter may be displayed in factpanel 230, and organized into a number of separate categories of typesof facts. An exemplary set of medical fact categories includescategories for problems, medications, allergies, social history,procedures and vital signs. However, it should be appreciated that anysuitable fact categories may be used, as aspects of the invention arenot limited in this respect. In addition, organization of facts intocategories is not required, and displays without such organization arepossible. As depicted in FIGS. 3A and 3B, in some embodiments GUI 200may be configured to provide a navigation panel 300, with a selectableindication of each fact category available in the display of fact panel230. In some embodiments, when the user selects one of the categorieswithin navigation panel 300 (e.g., by clicking on it with a mouse,touchpad, stylus, or other input device), fact panel 230 may be scrolledto display the corresponding fact category. As depicted in FIGS. 3A and3B, all available fact categories for the current document type aredisplayed, even if a particular fact category includes no extracted orotherwise entered medical facts. However, this is not required; in someembodiments, only those fact categories having facts ascertained fromthe patient encounter may be displayed in fact panel 230.

Fact panel 230 scrolled to the top of the display as depicted in FIG. 3Ashows problem fact category 310, medications fact category 320, andallergies fact category 330. Within problem fact category 310, fourclinical facts have been extracted from the example text narrative; noclinical facts have been extracted in medications fact category 320 orin allergies fact category 330. Within problem fact category 310, fact312 indicates that patient 122 is currently presenting with unspecifiedchest pain; that the chest pain is a currently presenting condition isindicated by the status “active”. Fact 314 indicates that patient 122 iscurrently presenting with shortness of breath. Fact 316 indicates thatthe patient has a history (status “history”) of unspecified essentialhypertension. Fact 318 indicates that the patient has a history ofunspecified obesity. As illustrated in FIG. 3A, each clinical fact inproblem fact category 310 has a name field and a status field. In someembodiments, each field of a clinical fact may be a structured componentof that fact represented as a discrete structured data item. In thisexample, the name field may be structured such that only a standard setof medical terms for problems may be available to populate that field.For example, the status field may be structured such that only statusesin the Systematized Nomenclature of Medicine (SNOMED) standard (e.g.,“active” and “history”) may be selected within that field, althoughother standards (or no standard) could be employed. An exemplary list offact categories and their component fields is given below. However, itshould be appreciated that this list is provided by way of example only,as aspects of the invention are not limited to any particularorganizational system for facts, fact categories and/or fact components.

-   -   Exemplary list of fact categories and component fields:    -   Category: Problems. Fields: Name, SNOMED status, ICD code.    -   Category: Medications. Fields: Name, Status, Dose form,        Frequency, Measures, RxNorm code, Administration condition,        Application duration, Dose route.    -   Category: Allergies. Fields: Allergen name, Type, Status, SNOMED        code, Allergic reaction, Allergen RxNorm.    -   Category: Social history—Tobacco use. Fields: Name, Substance,        Form, Status, Qualifier, Frequency, Duration, Quantity, Unit        type, Duration measure, Occurrence, SNOMED code, Norm value,        Value.    -   Category: Social history—Alcohol use. Fields: Name, Substance,        Form, Status, Qualifier, Frequency, Duration, Quantity,        Quantifier, Unit type, Duration measure, Occurrence, SNOMED        code, Norm value, Value.    -   Category: Procedures. Fields: Name, Date, SNOMED code.    -   Category: Vital signs. Fields: Name, Measure, Unit, Unit type,        Date/Time, SNOMED code, Norm value, Value.

In some embodiments, a linkage may be maintained between one or moremedical facts extracted by fact extraction component 104 and theportion(s) of the text narrative from which they were extracted. Asdiscussed above, such a portion of the text narrative may consist of asingle word or may include multiple words, which may be in a contiguoussequence or may be separated from each other by one or more interveningwords, sentence boundaries, section boundaries, or the like. Forexample, fact 312 indicating that patient 122 is currently presentingwith unspecified chest pain may have been extracted by fact extractioncomponent 104 from the words “chest pain” in the text narrative. The“active” status of extracted fact 312 may have been determined by factextraction component 104 based on the appearance of the words “chestpain” in the section of the text narrative with the section heading“Chief complaint”. In some embodiments, fact extraction component 104and/or another processing component may be programmed to maintain (e.g.,by storing appropriate data) a linkage between an extracted fact (e.g.,fact 312) and the corresponding text portion (e.g., “chest pain”).

In some embodiments, GUI 200 may be configured to provide visualindicators of the linkage between one or more facts displayed in factpanel 230 and the corresponding portion(s) of the text narrative in textpanel 220 from which they were extracted. In the example depicted inFIG. 3A, the visual indicators are graphical indicators consisting oflines placed under the appropriate portions of the text narrative intext panel 220. Indicator 313 indicates the linkage between fact 312 andthe words “chest pain” in the “Chief complaint” section of the textnarrative; indicator 315 indicates the linkage between fact 314 and thewords “shortness of breath” in the “Chief complaint” section of the textnarrative; indicator 317 indicates the linkage between fact 316 and theword “hypertensive” in the “Medical history” section of the textnarrative; and indicator 319 indicates the linkage between fact 318 andthe word “obese” in the “Medical history” section of the text narrative.However, these are merely examples of one way in which visual indicatorsmay be provided, as other types of visual indicators may be provided.For example, different or additional types of graphical indicators maybe provided, and/or linked text in text panel 220 may be displayed in adistinctive textual style (e.g., font, size, color, formatting, etc.).Aspects of the invention are not limited to any particular type oflinkage indicator.

In some embodiments, when the textual representation of the free-formnarration provided by clinician 120 has been re-formatted and factextraction has been performed with reference to the re-formattedversion, the original version may nevertheless be displayed in textpanel 220, and linkages may be maintained and/or displayed with respectto the original version. For example, in some embodiments, eachextracted clinical fact may be extracted by fact extraction component104 from a corresponding portion of the re-formatted text, but thatportion of the re-formatted text may have a corresponding portion of theoriginal text of which it is a formatted version. A linkage maytherefore be maintained between that portion of the original text andthe extracted fact, despite the fact actually having been extracted fromthe re-formatted text. In some embodiments, providing an indicator ofthe linkage between the extracted fact and the original text may allowclinician 120 and/or other user 150 to appreciate how the extracted factis related to what was actually said in the free-form narration.However, other embodiments may maintain linkages between extracted factsand the re-formatted text, as an alternative or in addition to thelinkages between the extracted facts and the original text, as aspectsof the invention are not limited in this respect.

Fact panel 230 scrolled to the bottom of the display as depicted in FIG.3B shows social history fact category 340, procedures fact category 350,and vital signs fact category 360. Within social history fact category340, two clinical facts have been extracted; no facts have beenextracted in procedures fact category 350 and vital signs fact category360. Within social history fact category 340, fact 342 indicates thatpatient 122 currently smokes cigarettes with a frequency of one pack perday. Fact 344 indicates that patient 122 currently occasionally drinksalcohol. Indicator 343 indicates that fact 342 was extracted from thewords “He smokes one pack per day” in the “Social history” section ofthe text narrative; and indicator 345 indicates that fact 344 wasextracted from the words “Drinks occasionally” in the “Social history”section of the text narrative. In some embodiments, visual indicatorssuch as indicators 343 and 345 may be of a different textual and/orgraphical style or of a different indicator type than visual indicatorssuch as indicators 313, 315, 317 and 319, to indicate that theycorrespond to a different fact category. For example, in someembodiments indicators 343 and 345 corresponding to social history factcategory 340 may be displayed in a different color than indicators 313,315, 317 and 319 corresponding to problems fact category 310. In someembodiments, linkages for different individual facts may be displayed indifferent textual and/or graphical styles or indicator types to allowthe user to easily appreciate which fact corresponds to which portion ofthe text narrative. For example, in some embodiments indicator 343 maybe displayed in a different color than indicator 345 because theycorrespond to different facts, even though both correspond to the samefact category.

In some embodiments, GUI 200 may be configured to allow the user toselect one or more of the medical facts in fact panel 230, and inresponse to the selection, to provide an indication of the portion(s) ofthe text narrative from which those fact(s) were extracted. An exampleis illustrated in FIG. 4. In this example, fact 312 (“unspecified chestpain”) has been selected by the user in fact panel 230, and in responsevisual indicator 420 of the portion of the text narrative from whichfact 312 was extracted (“chest pain”) is provided. Such a user selectionmay be made in any suitable way, as aspects of the invention are notlimited in this respect. Examples include using an input device (e.g.,mouse, keyboard, touchpad, stylus, etc.) to click on or otherwise selectfact 312, hovering the mouse or other input mechanism above or nearby tofact 312, speaking a selection of fact 312 through voice, and/or anyother suitable selection method. Similarly, in some embodiments GUI 200may be configured to visually indicate the corresponding fact in factpanel 230 when the user selects a portion of the text narrative in textpanel 220. In some embodiments, a visual indicator may include a line orother graphical connector between a fact and its corresponding portionof the text narrative. Any visual indicator may be provided in anysuitable form (examples of which are given above) as aspects of theinvention are not limited in this respect. In addition, aspects of theinvention are not limited to visual indicators, as other forms ofindicators may be provided. For example, in response to a user selectionof fact 312, an audio indicator of the text portion “chest pain” may beprovided in some embodiments. In some embodiments, the audio indicatormay be provided by playing the portion of the audio recording of theclinician's spoken dictation comprising the words “chest pain”. In otherembodiments, the audio indicator may be provided by playing an audioversion of the words “chest pain” generated using automatic speechsynthesis. Any suitable form of indicator or technique for providingindicators may be used, as aspects of the invention are not limited inthis respect.

In some embodiments, GUI 200 may be configured to provide any of variousways for the user to make one or more changes to the set of medicalfacts extracted from the text narrative by fact extraction component 104and displayed in fact panel 230, and these changes may be collected byfact review component 106 and applied to the documentation of thepatient encounter. For example, the user may be allowed to delete a factfrom the set in fact panel 230, e.g., by selecting the “X” optionappearing next to the fact. In some embodiments, the user may be allowedto edit a fact within fact panel 230. In one example, the user may editthe name field of fact 312 by selecting the fact and typing, speaking orotherwise providing a different name for that fact. As depicted in FIG.3A and FIG. 4, in some embodiments the user may edit the status field offact 312 by selecting a different status from the available drop-downmenu, although other techniques for allowing editing of the status fieldare possible. In some embodiments, the user may alternatively oradditionally be allowed to edit a fact by interacting with the textnarrative in text panel 220. For example, the user may add, delete, orchange one or more words in the text narrative, and then the textnarrative may be re-processed by fact extraction component 104 toextract an updated set of medical facts. In some embodiments, the usermay be allowed to select only a part of the text narrative in text panel220 (e.g., by highlighting it), and have fact extraction component 104re-extract facts only from that part, without disturbing facts alreadyextracted from other parts of the text narrative.

In some embodiments, GUI 200 may be configured to provide any of variousways for one or more facts to be added as discrete structured dataitems. As depicted in FIG. 4, GUI 200 in some embodiments may beconfigured to provide an add fact button for each fact categoryappearing in fact panel 230; one such add fact button is add fact button430. When the user selects add fact button 430, in some embodiments GUI200 may provide the user with a way to enter information sufficient topopulate one or more fields of a new fact in that fact category, forexample by displaying pop-up window 500 as depicted in FIG. 5. It shouldbe appreciated that this is merely one example, as aspects of theinvention are not limited to the use of pop-up windows or any otherparticular method for adding a fact. In this example, pop-up window 500includes a title bar 510 that indicates the fact category (“Problems”)to which the new fact will be added. Pop-up window 500 also provides anumber of fields 520 in which the user may enter information to definethe new fact to be added. Fields 520 may be implemented in any suitableform, including as text entry boxes, drop-down menus, radio buttonsand/or checkboxes, as aspects of the invention are not limited to anyparticular way of receiving input defining a fact. Finally, pop-upwindow 500 includes add button 530, which the user may select to add thenewly defined fact to the set of facts corresponding to the patientencounter, thus entering the fact as a discrete structured data item.

In some embodiments, GUI 200 may alternatively or additionally beconfigured to allow the user to add a new fact by selecting a (notnecessarily contiguous) portion of the text narrative in text panel 220,and indicating that a new fact should be added based on that portion ofthe text narrative. This may be done in any suitable way. In oneexample, the user may highlight the desired portion of the textnarrative in text panel 220, and right-click on it with a mouse (orperform another suitable input operation), which may cause thedesignated text to be processed and any relevant facts to be extracted.In other embodiments, the right-click or other input operation may causea menu to appear. In some embodiments the menu may include options toadd the new fact under any of the available fact categories, and theuser may select one of the options to indicate which fact category willcorrespond to the new fact. In some embodiments, an input screen such aspop-up window 500 may then be provided, and the name field may bepopulated with the words selected by the user from the text narrative.The user may then have the option to further define the fact through oneor more of the other available fields, and to add the fact to the set ofmedical facts for the patient encounter as described above.

In some embodiments, the set of medical facts corresponding to thecurrent patient encounter (each of which may have been extracted fromthe text narrative or provided by the user as a discrete structured dataitem) may be added to an existing electronic medical record (such as anEHR) for patient 122, or may be used in generating a new electronicmedical record for patient 122. In some embodiments, clinician 120and/or coding specialist (or other user) 150 may finally approve the setof medical facts before they are included in any patient record;however, aspects of the present invention are not limited in thisrespect. In some embodiments, when there is a linkage between a fact inthe set and a portion of the text narrative, the linkage may bemaintained when the fact is included in the electronic medical record.In some embodiments, this linkage may be made viewable by simultaneouslydisplaying the fact within the electronic medical record and the textnarrative (or at least the portion of the text narrative from which thefact was extracted), and providing an indication of the linkage in anyof the ways described above. Similarly, extracted facts may be includedin other types of patient records, and linkages between the facts in thepatient records and the portions of text narratives from which they wereextracted may be maintained and indicated in any suitable way.

A CLU system in accordance with the techniques described herein may takeany suitable form, as aspects of the present invention are not limitedin this respect. An illustrative implementation of a computer system 600that may be used in connection with some embodiments of the presentinvention is shown in FIG. 6. One or more computer systems such ascomputer system 600 may be used to implement any of the functionalitydescribed above. The computer system 600 may include one or moreprocessors 610 and one or more tangible, non-transitorycomputer-readable storage media (e.g., volatile storage 620 and one ormore non-volatile storage media 630, which may be formed of any suitablenon-volatile data storage media). The processor 610 may control writingdata to and reading data from the volatile storage 620 and thenon-volatile storage device 630 in any suitable manner, as the aspectsof the present invention are not limited in this respect. To perform anyof the functionality described herein, the processor 610 may execute oneor more instructions stored in one or more computer-readable storagemedia (e.g., volatile storage 620), which may serve as tangible,non-transitory computer-readable storage media storing instructions forexecution by the processor 610.

Computer-Assisted Coding (CAC) System

As discussed above, medical coding has conventionally been a manualprocess whereby a human professional (the “coder”) reads all of thedocumentation for a patient encounter and enters the appropriatestandardized codes (e.g., ICD codes, HCPCS codes, etc.) corresponding tothe patient's diagnoses, procedures, etc. The coder is often required tounderstand and interpret the language of the clinical documents in orderto identify the relevant diagnoses, etc., and assign them theircorresponding codes, as the language used in clinical documentationoften varies widely from the standardized descriptions of the applicablecodes. For example, the coder might review a hospital report saying,“The patient coded at 5:23 pm.” The coder must then apply the knowledgethat “The patient coded” is hospital slang for a diagnosis of “cardiacarrest,” which corresponds to ICD-9-CM code 427.5. This diagnosis couldnot have been identified from a simple word search for the term “cardiacarrest,” since that standard term was not actually used in thedocumentation; more complex interpretation is required in this example.

As also discussed above, conventional medical coding systems may providea platform on which the human coder can read the relevant documents fora patient encounter, and an interface via which the human coder canmanually input the appropriate codes to assign to the patient encounter.By contrast, some embodiments described herein may make use of a type ofmedical coding system referred to herein as a “computer-assisted coding”(CAC) system, which may automatically analyze medical documentation fora patient encounter to interpret the document text and derivestandardized codes hypothesized to be applicable to the patientencounter. The automatically derived codes may then be suggested to thehuman coder, clinician, or other user of the CAC system. In someembodiments, the CAC system may make use of an NLU engine to analyze thedocumentation and derive suggested codes, such as through use of one ormore components of a CLU system such as exemplary system 100 describedabove. In some embodiments, the NLU engine may be configured to derivestandardized codes as a type of medical fact extracted from one or moredocuments for the patient encounter, and/or the CLU system may beconfigured to access coding rules corresponding to the standardized codeset(s) and apply the coding rules to extracted medical facts to derivethe corresponding codes.

In some embodiments, the CAC system may be configured to provide a userinterface via which the automatically suggested codes may be reviewed bya user such as a medical coder. The user interface may take on any ofnumerous forms, and aspects of the invention are not limited to anyparticular implementation Like the user interfaces for the CLU system100 described above, the user interface for the CAC system may providetools that allow a coder to interact with the CAC system in any suitableform, including visual forms, audio forms, combined forms, or any otherform providing the functionality described herein. When the tools areprovided in visual form, their functionality may be accessed in someembodiments through a graphical user interface (GUI), which may beimplemented in any suitable way. An example of a suitable GUI 700 for aCAC system is illustrated in FIG. 7A.

The exemplary GUI 700 provides the user with the ability tosimultaneously view the list of codes for a patient encounter along withthe documentation from which the codes are derived. Some embodiments mayalso allow the user to view structured encounter- or patient-level datasuch as the patient's age, gender, etc. (not shown in FIG. 7A), some orall of which information may be useful in arriving at the appropriatecodes for the patient encounter. In panel 710 is displayed a list ofavailable documents for the patient encounter currently being coded. Inthe example illustrated in FIG. 7A, these include two History & Physicalreports, a Discharge Summary, an Emergency Room Record, a Consultationreport, a Progress Note, and an Operative Report. Indicator 712 showsthat the current document being viewed is the Discharge Summary datedJun. 18, 2014, and this document appears in panel 720 where the user canview the text of the document. Shown in panel 730 is the current list ofcodes for the patient encounter. An indicator 732 shows, for each codein the list, whether the code was automatically suggested or addedmanually by the user. In this particular example, the empty circlesindicate that all of the codes in the current list were automaticallysuggested by the CAC system.

Exemplary GUI 700 also provides the user with the ability to view and/orquery which portion(s) of the available documentation gave rise to thesuggestion of which code(s) in the list of codes for the patientencounter. In some embodiments, any suitable indicator(s) may beprovided of the link between a particular code and the portion(s) of thedocumentation text from which the code was derived. Each automaticallysuggested code may be linked to one or more portions of text from whichthe code was derived, and each linked portion of text may be linked toone or more codes that are derivable from that portion of text. Forinstance, viewing together FIGS. 7A and 7D, which show the DischargeSummary viewed at different scroll locations in panel 720, it can beseen that there are two different mentions of “respiratory failure” inthe document from which code 518.81 may have been derived (an example ofa link between a code and multiple portions of text), and that there aretwo different codes 303.90 and 571.5 that may have been derived at leastin part from the mention of “Alcoholism” in the text (an example of alink between a portion of text and multiple codes).

In the example of FIG. 7A, an indicator 722 is provided (underlining inthis particular example) to visually distinguish portions of thedocument text linked to codes in the current list. Exemplary GUI 700also allows the user to query a particular linked portion of text to seewhich code(s) are linked to that portion of text. FIG. 7B illustrates anexemplary indicator 724 of the corresponding link that may be displayedin response to the user querying the linked portion of text in anysuitable way, such as by selecting or hovering over it with the mousepointer. Exemplary GUI 700 further allows the user to query a particularcode to see which portion(s) of text are linked to that code. FIG. 7Cillustrates an exemplary way of querying code 287.5 by right-clicking onthe listed code in panel 730 and selecting “Show Highlights” in thecontext menu that then appears. In response, the document in which thelinked text appears is displayed in panel 720 (in this case it is thesame Discharge Summary, scrolled to a particular section), and thelinked text is visually distinguished by indicator 726 (highlighting inthis particular example), as illustrated in FIG. 7D.

If the user disagrees with the linked text and does not believe that thesuggested portion(s) of text actually should correspond with the linkedcode, the user can select “Unlink Text” in the context menu of FIG. 7Cto cause the link between that code and the corresponding text to bediscarded. The user can also manually create a new link between a codeand one or more portions of text, e.g., by selecting “Link Text” in thecontext menu of FIG. 7C and highlighting or otherwise designating theportion(s) of text in the documentation which should be linked to theselected code.

Exemplary GUI 700 further allows the user to accept or reject each ofthe automatically suggested codes, e.g., using the context menu of FIG.7C for each suggested code. FIG. 7E illustrates exemplary indicators 734and 736 which replace indicator 732 for each code that has been acceptedor rejected, respectively. In this example, the user has accepted mostof the suggested codes, but has rejected code 571.5 because the userbelieves the mention of “Alcoholism” in the documentation makes thediagnosis of “Cirrhosis of Liver w/o Alcohol” incorrect. Exemplary GUI700 further allows the user to provide a reason for the rejection of acode, such as by using the exemplary context menu illustrated in FIG.7F. In some embodiments, the reasons provided by users for rejectingparticular automatically suggested codes may be used for review and/ortraining purposes (e.g., for training the NLU engine, e.g., of the CLUsystem to derive more accurate codes from documentation text).

GUI 700 may also allow the user to replace a code with a different code,instead of rejecting the code outright, e.g., using the context menu ofFIG. 7C. In the example illustrated in FIG. 7E, the user has replacedcode 482.9 with code 482.1, and indicator 738 shows that the new codewas user-added. 482.9 (Pneumonia due to Pseudomonas) is a more specificdiagnosis applicable to the patient encounter than the suggested 482.1(Bacterial Pneumonia, Unspecified), so the user may provide “Morespecific code needed” as the reason for the replacement. In someembodiments, when a user replaces an automatically suggested code with adifferent code, any documentation text that was linked to the originallysuggested code may then be linked to the replacement code. Suchreplacement codes, optionally with linked text and/or replacementreasons, may also be used as feedback, e.g., for training of the CLUsystem.

The user can also add a code to the list for a patient encounter bymanually inputting the code in input field 740. For example, FIG. 7Eshows a new code 041.7 that has been added by the user. The user maylink the added code to supporting portion(s) of the text, such as themention of “pseudomonas” in the Discharge Summary, e.g., by using the“Link Text” procedure described above.

When the user has completed the review of the codes and supportingdocumentation, exemplary GUI 700 allows the user to submit the codes forfinalization by selecting button 750. FIG. 8 illustrates an exemplarycode finalization screen 800 that may be displayed following the user'sselection of submit button 750. In exemplary screen 800, all of theaccepted and user-added codes are displayed for final review.Alternatively, in some embodiments the user may be required toaffirmatively accept even user-added codes before they will appear incode finalization screen 800. The codes are displayed in screen 800 inan ordered sequence, which the user may change by re-ordering the codes.In some embodiments, the order of the finalized sequence of codes may beused in later processes such as billing, to determine the principaldiagnosis, etc. Exemplary screen 800 also includes fields for “presenton admission” (POA) indicators, which provide information on whethereach diagnosis was present when the patient was admitted to thehospital, or was acquired during the hospital stay. This information maybe required documentation in some circumstances, and in some embodimentsmay be used for review and/or training purposes. In some embodiments,POA indicators may be automatically suggested, e.g., using the CLUsystem; while in other embodiments, POA indicators may only be inputmanually.

When the user is satisfied with the finalized sequence of codes,exemplary screen 800 provides a button 810 for the codes to be saved, atwhich the coding process for the patient encounter becomes complete. Insome embodiments, the CAC system may compare the finalized sequence ofcodes with stored coding rules, and may present the user with anyapplicable error or warning notifications prior to saving. As discussedabove, once saved, the finalized sequence of codes may be sent to otherprocesses such as billing and quality review, and in some embodimentsmay be used for performance review and/or training of the CLU and/or CACsystems.

Like the embodiments of the CLU system 100 described above, the CACsystem in accordance with the techniques described herein may take anysuitable form, as embodiments are not limited in this respect. Anillustrative implementation of a computer system 900 that may be used inconnection with some implementations of a CAC system is shown in FIG. 9.One or more computer systems such as computer system 900 may be used toimplement any of the functionality of the CAC system described above. Asshown, the computer system 900 may include one or more processors 910and one or more tangible, non-transitory computer-readable storage media(e.g., volatile storage 920 and one or more non-volatile storage media930, which may be formed of any suitable non-volatile data storagemedia). The processor 910 may control writing data to and reading datafrom the volatile storage 920 and the non-volatile storage media 930 inany suitable manner, as the aspects of the present invention are notlimited in this respect. To perform any of the functionality describedherein, the processor 910 may execute one or more instructions stored inone or more computer-readable storage media (e.g., volatile storage920), which may serve as tangible, non-transitory computer-readablestorage media storing instructions for execution by the processor 910.

Annotation of Richly Formatted Documents

According to an aspect of the present application a system and methodare provided for annotating medical documents having a rich format andpresenting annotated documents to a user with rich formatting. Referringto FIG. 10, a document flow 1000 may include an XHTML document 1002representing a medical document and also representing the structure andformatting of a richly formatted document, described further below inconnection with rich text document 1001. The medical document may beassociated with a clinical patient encounter, and may represent aclinician's notes, a report such as a laboratory or radiology report, ormay be any other suitable type of document. As described above, theXHTML document 1002 may represent (or include) rich formatting, such asheadings, bold characters, characters in italics, underlining,highlighting, bullet lists, or other formatting.

In at least some instances, an initial medical document may not be inXHTML format. Rather, an initial richly formatted document, such as richtext document 1001 (e.g., representing doctor's notes), may have richformatting (e.g., of the types described above in connection withdocument 1002) but may not be a XHTML document. In such instances, therich text document 1001 (or other richly formatted document) may beconverted to the XHTML document 1002 using any suitable conversiondevice and process.

It may be desirable to annotate the medical document represented by theXHTML document 1002, for instance by generating medical codescorresponding to information expressed in the document. For example, ifthe medical document represents a summary of a patient encounter, it maybe desirable to identify codes implicated by the document relating todiagnoses, medical procedures (including treatments), or other medicallyrelevant information. Such codes may, in some instances, be used forbilling purposes and therefore may be medical billing codes. However,other types of annotations, not limited to codes, (such as other typesof medical facts that can be extracted by a CLU system as discussedabove) may be used as well. As discussed above, the term “annotation” asused herein refers to an item derived from and linked to a portion oftext, such as a fact (e.g., a medical fact, one particular example ofwhich may be a medical code such as a medical billing code), a semanticlabel, or other such item having a link to one or more correspondingportions of text from which it was or could be derived.

It may also be desirable in some embodiments to annotate the medicaldocument automatically (rather than manually). An NLU engine (e.g.,forming part of a CLU system) may be used for such purposes. However,NLU engines conventionally are unable to handle rich formatting, beinglimited to operating on plain text documents. Thus, to annotate theXHTML document 1002, a plain text document 1004 may be generated, forexample by extracting the textual information from the XHTML document1002. Such extraction may include extracting the text nodes from theXHTML document 1002, which may be strung together to form a narrativeplain text document represented by plain text document 1004. The plaintext document 1004 may retain the content of the XHTML document 1002 butnot the formatting.

In generating the plain text document 1004, links may be generatedbetween the span nodes of the XHTML document 1002 and the correspondingposition in the plain text document 1004. These links may be maintainedto allow later mapping back of annotations from the plain text document1004 into the XHTML document 1002, as described further below.

The plain text document 1004 may then be provided to a CLU system 1006having an NLU engine. The CLU system may annotate the plain textdocument 1004 in any suitable manner. For example, in some embodimentsthe CLU system 1006 may derive applicable medical codes from the plaintext document. The medical codes may be any of those types describedherein (e.g., medical diagnosis codes, medical procedure or treatmentcodes, other medical billing codes, etc.) or any other suitable codetypes. Also, annotations other than codes may alternatively oradditionally be generated in some embodiments.

The CLU system 1006 may generate or provide (as an output) a tokenizeddocument and a document containing annotations. The tokenized documentmay be, in some embodiments, a tokenized XHTML document. Such a documentmay be generated by tokenizing words and phrases in the document.Character offsets of the tokens may be returned by the CLU system 1006.The document containing annotations may be any suitable document, anon-limiting example of which is the illustrated application XML 1008.Thus, it should be appreciated that the annotations may be maintained ina separate document from the tokenized document.

Evidence, tied to codes (e.g., medical codes of the types describedabove) may also be returned by the CLU system 1006. The evidence mayrepresent support for the generated codes, such as text in the documentidentified as supporting the generation of a particular code. Thus,evidence may be in the form of one or more portions of a document beingannotated. The evidence, or location (document position, offset)information for the evidence, may be identified in the documentcontaining the annotations (e.g., in the application XML 1008).

The tokenized XHTML document and application XML 1008 may be used topresent an annotated XHTML document 1010. That is, the annotations fromthe application XML 1008 may be applied to the tokenized XHTML documentoutput by the CLU system. In some embodiments, the annotations areapplied using an application viewer which presents the annotateddocument to a user. The annotated XHTML document 1010 thereforerepresents a richly formatted document including the annotations fromCLU system 1006. In some embodiments, the presented annotated richlyformatted document is transient in that there may be no documentmaintained by the system which includes the annotations in a richlyformatted document. Rather, in such non-limiting embodiments, theannotations may be applied only in the transient document presented tothe user (e.g., via a browser or other viewing application).

In some embodiments, the annotations are applied to the tokenized XHTMLoutput by the CLU system by suitable restructuring of the tokenizedXHTML document. For example, the nodes (e.g., the leaf nodes) in theXHTMl document 1002 may be restructured to reflect (or capture) tokensfrom the CLU system 1006 and the token offsets generated by the CLUsystem 1006 may be mapped back to nodes of the XHTML document asindividual span tags that are assigned (unique) identifiers. Othermanners of generating the annotated XHTML document are also possible.

By rendering the XHTML document 1010 (e.g., using any suitable renderingdevice with a suitable user interface such as or similar to userinterface 110), a user may view an annotated version of the medicaldocument with rich formatting. For example, background styles of thetokens that are part of an annotation generated by the CLU system 1006may be changed to indicate that the CLU system 1006 considers suchtokens to represent evidence for the annotations. In this manner,formatting representing evidence from the CLU system 1006 may beoverlaid on the original rich formatting of the XHTML document 1002. Byretaining the formatting of the medical document, the user may moreeasily understand the basis for certain annotations and therefore may beable to more easily evaluate and/or correct the annotations than if theannotations were presented in a plain text version.

Any suitable system may be used to implement the document flow of FIG.10. For example, a CLU system of the types described herein may be used.In some embodiments, the document flow may be implemented as part of aCAC system.

FIG. 11 illustrates a method which may be used to generate an annotateddocument in connection with the document flow of FIG. 10, according to anon-limiting embodiment of the present application. The method 1100 maybegin at stage 1110 by converting an XHTML document structured torepresent (or have) rich formatting into a plain text document. TheXHTML document may be XHTML document 1002 described in connection withFIG. 10 and thus may represent a medical document in some embodiments,although other types of documents may alternatively be used. The plaintext document may be plain text document 1004 and may be generated inthe manner previously described in connection with FIG. 10 (e.g., byextracting and stringing together text nodes from the XHTML document) orin any other suitable manner.

At stage 1120, the plain text document may be provided to a CLU system(e.g., CLU system 1006 in FIG. 10). The CLU system may tokenize andannotate the plain text document, thus producing, at stage 1130 of themethod 1100, a tokenized document (e.g., a tokenized XHTML document) anda document containing annotations (e.g., an application XML) of thetypes described in connection with FIG. 10.

At stage 1140, the tokenized document and application XML may be used toannotate the XHTML document. For example, as described in connectionwith FIG. 10, the nodes in the XHTML document may be restructured basedon the tokenization of the plain text document by the CLU system in amanner that reflects the tokens. The background styles of the XHTMLdocument may be changed for tokens associated with annotations of theplain text document by the CLU system, thus providing evidence to theXHTML document. The annotation of the XHTML document may be performed aspart of a step of rendering the document for a user, for example usingan application viewer (e.g., a CAC application viewing program). Thus,the method may provide the user with an annotated version of theoriginal document (e.g., an original medical record) in rich formatting.

FIGS. 12A-12C illustrate non-limiting examples of documents which may beused or associated with the document flow of FIG. 10 and/or the methodof FIG. 11. FIG. 12A illustrates a non-limiting example of a tokenizedXHTML document 1200 which may be output by a CLU system as describedabove in connection with FIG. 10. The tokenized XHTML has richformatting, including headings 1202 which, in this non-limiting example,include bold text. It can be seen from FIG. 12A that the text of thetokenized XHTML document 1200 is arranged into sections delineated bythe headings 1202. Also, content arranged in bullet lists (e.g., bulletlist 1204) is included. The tokenized XHTML document 1200 may reflect(substantially or entirely) the formatting of an original clinicaldocument (e.g., rich text document 1001) input to the CLU system, andthus the original rich text document is not shown separately.

FIG. 12B is a non-limiting example of a document 1210 includingannotations output by a CLU system. In this non-limiting example, theannotations relate to ICD9 codes as indicated by the “coding systemtype” heading 1212, and thus the illustrated document represents anexample of an output from a CAC system. The document 1210 includes adocument ID 1214, as well as annotation IDs 1216 identifying distinctannotations. The annotations are specific to tokens 1218, differentiatedby token offsets 1220 as previously described in connection with FIG.10.

The document 1210 also includes code ID 1222 representing a codeannotation for the document, which in this case is a code (V76.51)corresponding to an ICD9 code. It should be appreciated that other codesand other annotations could alternatively be used.

The document 1210 represents a partial document of annotations, and inpractice such a document may be larger or smaller than that shown.

FIG. 12C illustrates an example of a rendered document 1250 which may bepresented to a user (e.g., by a CAC application viewer or other suitableviewing application) and which represents annotations displayed withrich formatting. In this sense, the document 1250 may represent theannotated XHTML document 1010 of FIG. 10, as a non-limiting example.

As shown, the document 1250 is substantially the same as document 1200of FIG. 12A except for the addition of an annotated code 1252 (codeV76.51) and highlighting 1254. The highlighting 1254 may serve asevidence supporting the annotation 1252, so that a user (e.g., a humanreviewer) may assess the accuracy of the annotation 1252. It should beappreciated that presentation of the annotation in the form shown, withrich formatting, may aid review of the annotation compared to if theannotation had been presented as part of a plain text document.

FIG. 13 illustrates an alternative document flow to that of FIG. 10 inwhich tabular content is included, according to a non-limitingembodiment of the present application. As described previously inconnection with FIG. 10, a document to be annotated may include richformatting. In some embodiments, the document may include tabular data.It may be desirable to retain the tabular data and formatting whenpresenting the user with an annotated version of the document. FIG. 13illustrates a document flow which may be used when tabular content isinvolved.

As shown, the document flow 1300 retains elements of the document flow1000, which are not described again in detail here. In addition to theplain text document 1004, a tabular document 1302 is generated from theXHTML document 1002 by extracting any tables in the XHTML document 1002.Both the plain text document 1004 and the tabular document 1302 areprovided to the CLU system 1006. As with the plain text document 1004,the CLU system annotates the tabular document 1302.

The CLU system 1006 generates a tokenized document and application XML1304 similar to that described above in connection with FIG. 10, thedifference being that the tabular content is included in the tokenizeddocument and application XML 1304. The tokenized document andapplication XML 1304 is then used to annotate the XHTML document 1002 inthe manner previously described in connection with FIG. 10, thusresulting in an annotated XHTML document 1306. As with the annotatedXHTML document 1010, the annotated XHTML document 1306 may be a documentpresented to a user (e.g., via a suitable viewing application) and may,in some embodiments, be transient. That is, in some embodiments, thepresented annotated richly formatted document is not maintained by thesystem.

FIG. 14 illustrates a scheme for developing and providing training datafor a CLU system, according to a non-limiting embodiment of the presentapplication. The scheme expands upon the document flows of FIGS. 10 and13 and the associated methods. Those elements which FIGS. 10 and 13 havein common with FIG. 14 are not described again in detail here.

As previously described in connection with FIG. 13, an annotated XHTMLdocument 1306 may be generated. A user may be able to view the documentwhen suitably rendered, for example in a suitable user interface. Anon-limiting example may be the rendered document 1250 of FIG. 12C. Theuser may be a medical coding specialist, a medical reviewer, or anyother type of user who may use such documents. It may be desirable, insome situations, for the user to edit (or annotate) the annotations ofthe XHTML document 1306. For example, the annotated XHTML document 1306may include annotations suggested by the CLU system 1006 (e.g.,annotation 1252 shown in FIG. 12C). The user may disagree with one ormore of the suggested annotations, may wish to add one or moreannotations not suggested by the CLU system 1006, or may wish to furtherannotate the annotated XHTML document for any other reason.

Aspects of the present application allow a user to view the annotationsof an annotated medical document in rich formatting, and/or to annotatethe document in rich formatting. For example, the user viewing theannotated XHTML document 1306 may generate user annotations 1402 of thedocument. The user annotations 1402 may include corrected or additionalmedical codes or other forms of annotations. The user annotations mayalso include supporting evidence, which the user may generate by, forexample, selecting appropriate portions of the XHTML document. Suchselection may be performed by highlighting the appropriate portions,underlining the appropriate portions, selecting one or more items from amenu (e.g., a drop-down menu presented on the user interface), or in anyother suitable manner. As a non-limiting example, a user viewing thedocument 1250 may wish to add a code not suggested by the CLU system. Todo so, the user may highlight text of the document 1250 and then bepresented with an input box on a GUI in which to type the code to beadded corresponding to the highlighted text. Other forms of userannotations are also possible, as this is a non-limiting example.

The user annotations 1402 may be provided back to the CLU system 1006 asshown in FIG. 14, and the CLU system 1006 may be trained (or re-trained,as the case may be) accordingly. For example, the user may select text(contiguous or non-contiguous) in the annotated XHTML document 1306 andassociate the selected text with a code (e.g., a medical diagnosis codeor medical procedure code). Tokens associated with the selected text maybe mapped from the span identifiers back to the offsets in the plaintext document, described previously herein. This feedback may be usedfor purposes of training the CLU system 1006.

The aspects of the present application may provide one or more benefits,some of which have been previously described. Now described are somenon-limiting examples of such benefits. It should be appreciated thatnot all aspects and embodiments necessarily provide all of the benefitsnow described. Further, it should be appreciated that aspects of thepresent application may provide additional benefits to those nowdescribed.

As has been described, aspects of the present application provide forpresentation of annotated medical or other clinical documents in richformatting. The rich formatting may facilitate review and analysis ofthe annotated documents, for example by providing the reviewer (e.g., ahuman user) with valuable formatting information. Accordingly, reviewand analysis of annotations may be made more accurate. Also, entry ofnew annotations or corrections from the user may be simplified and mademore accurate.

The above-described embodiments of the present invention can beimplemented in any of numerous ways. For example, the embodiments may beimplemented using hardware, software or a combination thereof. Whenimplemented in software, the software code can be executed on anysuitable processor or collection of processors, whether provided in asingle computer or distributed among multiple computers. It should beappreciated that any component or collection of components that performthe functions described above can be generically considered as one ormore controllers that control the above-discussed functions. The one ormore controllers can be implemented in numerous ways, such as withdedicated hardware, or with general purpose hardware (e.g., one or moreprocessors) that is programmed using microcode or software to performthe functions recited above.

In this respect, it should be appreciated that one implementation ofembodiments of the present invention comprises at least onecomputer-readable storage medium (i.e., a tangible, non-transitorycomputer-readable medium, such as a computer memory, a floppy disk, acompact disk, a magnetic tape, or other tangible, non-transitorycomputer-readable medium) encoded with a computer program (i.e., aplurality of instructions), which, when executed on one or moreprocessors, performs above-discussed functions of embodiments of thepresent invention. The computer-readable storage medium can betransportable such that the program stored thereon can be loaded ontoany computer resource to implement aspects of the present inventiondiscussed herein. In addition, it should be appreciated that thereference to a computer program which, when executed, performs any ofthe above-discussed functions, is not limited to an application programrunning on a host computer. Rather, the term “computer program” is usedherein in a generic sense to reference any type of computer code (e.g.,software or microcode) that can be employed to program one or moreprocessors to implement above-discussed aspects of the presentinvention.

The phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” “having,” “containing”, “involving”, andvariations thereof, is meant to encompass the items listed thereafterand additional items. Use of ordinal terms such as “first,” “second,”“third,” etc., in the claims to modify a claim element does not byitself connote any priority, precedence, or order of one claim elementover another or the temporal order in which acts of a method areperformed. Ordinal terms are used merely as labels to distinguish oneclaim element having a certain name from another element having a samename (but for use of the ordinal term), to distinguish the claimelements from each other.

Having described several embodiments of the invention in detail, variousmodifications and improvements will readily occur to those skilled inthe art. Such modifications and improvements are intended to be withinthe spirit and scope of the invention. Accordingly, the foregoingdescription is by way of example only, and is not intended as limiting.The invention is limited only as defined by the following claims and theequivalents thereto.

What is claimed is:
 1. A method comprising: converting a medicaldocument, having textual content and rich formatting, into a first XHTMLdocument that retains the textual content and the rich formatting of themedical document, the first XHTML document including a first text stringin rich formatting; generating from the first XHTML document a plaintext document that retains the textual content and not the richformatting of the first XHTML document, wherein generating the plaintext document comprises extracting text nodes from the first XHTMLdocument and forming the plain text document from the extracted textnodes, the plain text document including the first text string in plaintext without rich formatting; generating one or more annotations of theplain text document by applying a natural language understanding (NLU)engine implemented on a processor to the plain text document, the one ormore annotations including a first annotation linked to the first textstring in the plain text document without rich formatting; anddisplaying an annotated XHTML document created by applying the one ormore annotations of the plain text document to a tokenized XHTMLdocument including the textual content and the rich formatting of themedical document, the tokenized XHTML document including the first textstring in rich formatting, wherein displaying the annotated XHTMLdocument comprises displaying the first annotation linked to the firsttext string in rich formatting in the tokenized XHTML document.
 2. Themethod of claim 1, wherein applying the one or more annotations of theplain text document to the tokenized XHTML document is performed as partof a rendering process in which the tokenized XHTML document is renderedin a user interface.
 3. The method of claim 1, further comprisinggenerating the tokenized XHTML document by tokenizing the first XHTMLdocument.
 4. The method of claim 1, further comprising maintaining theone or more annotations in a document separate from the tokenized XHTMLdocument.
 5. The method of claim 1, wherein the one or more annotationscomprise a medical code.
 6. The method of claim 1, further comprising:receiving from a user, via the user interface, a selection of a portionof the annotated XHTML document and an annotation corresponding to theportion; and generating tag data mapping the annotation corresponding tothe portion to one or more locations in the tokenized XHTML documentcorresponding to the portion.
 7. The method of claim 6, furthercomprising providing the tag data to the NLU engine and re-training theNLU engine using the tag data and the plain text document.
 8. Acomputer-readable storage medium having instructions that, when executedby a processor, cause performance of a method comprising: converting amedical document, having textual content and rich formatting, into afirst XHTML document that retains the textual content and the richformatting of the medical document, the first XHTML document including afirst text string in rich formatting; generating from the first XHTMLdocument a plain text document that retains the textual content and notthe rich formatting of the first XHTML document, wherein generating theplain text document comprises extracting text nodes from the first XHTMLdocument and forming the plain text document from the extracted textnodes, the plain text document including the first text string in plaintext without rich formatting; generating one or more annotations of theplain text document by applying a natural language understanding (NLU)engine implemented on a processor to the plain text document, the one ormore annotations including a first annotation linked to the first textstring in the plain text document without rich formatting; anddisplaying an annotated XHTML document created by applying the one ormore annotations of the plain text document to a tokenized XHTMLdocument including the textual content and the rich formatting of themedical document, the tokenized XHTML document including the first textstring in rich formatting, wherein displaying the annotated XHTMLdocument comprises displaying the first annotation linked to the firsttext string in rich formatting in the tokenized XHTML document.
 9. Thecomputer-readable storage medium of claim 8, wherein applying the one ormore annotations of the plain text document to the tokenized XHTMLdocument is performed as part of a rendering process in which thetokenized XHTML document is rendered in a user interface.
 10. Thecomputer-readable storage medium of claim 8, wherein the method furthercomprises generating the tokenized XHTML document by tokenizing thefirst XHTML document.
 11. The computer-readable storage medium of claim8, wherein the method further comprises maintaining the one or moreannotations in a document separate from the tokenized XHTML document.12. The computer-readable storage medium of claim 8, wherein the one ormore annotations comprise a medical code.
 13. The computer-readablestorage medium of claim 8, wherein the method further comprises:receiving from a user, via the user interface, a selection of a portionof the annotated XHTML document and an annotation corresponding to theportion; and generating tag data mapping the annotation corresponding tothe portion to one or more locations in the tokenized XHTML documentcorresponding to the portion.
 14. The computer-readable storage mediumof claim 13, wherein the method further comprises providing the tag datato the NLU engine and re-training the NLU engine using the tag data andthe plain text document.
 15. A system, comprising: a processor; and amemory coupled to the processor and storing computer-readableinstructions which, when executed by the processor, cause performance ofa method comprising: converting a medical document, having textualcontent and rich formatting, into a first XHTML document that retainsthe textual content and the rich formatting of the medical document, thefirst XHTML document including a first text string in rich formatting;generating from the first XHTML document a plain text document thatretains the textual content and not the rich formatting of the firstXHTML document, wherein generating the plain text document comprisesextracting text nodes from the first XHTML document and forming theplain text document from the extracted text nodes, the plain textdocument including the first text string in plain text without richformatting; generating one or more annotations of the plain textdocument by applying a natural language understanding (NLU) engineimplemented on a processor to the plain text document, the one or moreannotations including a first annotation linked to the first text stringin the plain text document without rich formatting; and displaying anannotated XHTML document created by applying the one or more annotationsof the plain text document to a tokenized XHTML document including thetextual content and the rich formatting of the medical document, thetokenized XHTML document including the first text string in richformatting, wherein displaying the annotated XHTML document comprisesdisplaying the first annotation linked to the first text string in richformatting in the tokenized XHTML document.
 16. The system of claim 15,wherein applying the one or more annotations of the plain text documentto the tokenized XHTML document is performed as part of a renderingprocess in which the tokenized XHTML document is rendered in a userinterface.
 17. The system of claim 15, wherein the method furthercomprises generating the tokenized XHTML document by tokenizing thefirst XHTML document.
 18. The system of claim 15, wherein the methodfurther comprises maintaining the one or more annotations in a documentseparate from the tokenized XHTML document.
 19. The system of claim 15,wherein the one or more annotations comprise a medical code.
 20. Thesystem of claim 15, wherein the method further comprises: receiving froma user, via the user interface, a selection of a portion of theannotated XHTML document and an annotation corresponding to the portion;and generating tag data mapping the annotation corresponding to theportion to one or more locations in the tokenized XHTML documentcorresponding to the portion.